Glossary of terms

Term

Definition

Aetiological Studies

A study that looks at risk factors for a specific condition.

Analogue Scales

Data collected in a continuous form. Most common are visual analogue scales to measure pain (or other subjective constructs), where a 10 cm line is provided with only two clues and patients are asked to rate their pain by placing a mark along the line.  See example below. Also note: pain scales can be categorical (none, mild, moderate, severe etc.) and ordinal (rate your pain between 0 and 10).

Analytical Scale

These studies try to analyse a phenomena, and can be divided into two types, observational and experimental. Analytical studies are an attempt to identify strength of causality, by looking for relationships between different things, i.e. usually an intervention and an outcome.

Background Questions

Clinical questions ask for general knowledge about a condition or thing. They generally use a population observation format.

Biases

A systematic variation, i.e. not due to chance, usually due to problems in the research methodology. There are numerous ways in which research can be biased, and many biases occur even before data collection has commenced. It is essential that potential study biases are considered, recognised and addressed by researchers so that the research they conduct produces the least biased findings possible. An excellent reference to research bias is from Sackett (1979). This paper lists 35 common forms of bias in analytical studies.

It is important for researchers to have a clear understanding of whether bias has potentially affected study findings. This is why readers of research should be critical about study design and conduct, and research data interpretation. We provide information on a few common biases below.

Sackett D (1979): Bias in analytic research.  J Chron Dis 32: 51-63  

Hawthorn effect: Subjects modify their behaviors in response to being involved in an experimental study, not as a result of an experimental intervention.

Measurement Error: Error incurred in taking study measures. Error can occur from instrument error and investigator error. Subjects can contribute to repeated study measures being different, however this is not error, it is subject variability (subjects are rarely wrong). Error can be reduced by using standard protocols, and ensuring that the measurement instrument behaves the same way every time it is used, for every researcher and every patient.

Placebo effect: It is well established in drug trials that up to 30% control subjects may demonstrate the same amount of improvement in outcome measures as intervention subjects. There is little known about control subjects' placebo response in therapy trials. 

Biases - controlling them

There are several key ways to control for biases in research. We list some of these below. 

Alternative hypothesis: This analysis framework is often used in observational research. The alternative hypothesis underpins a cause and effect study, in which the study question is posed using a rational justified a priori understanding of a relationship. Bradford Hill (1965) presented the minimal conditions needed to establish a causal relationship between two measures (cause and effect): Temporal Relationship, Strength, Dose-Response Relationship, Consistency, Plausibility, Consideration of Alternate Explanations, Experiment, Specificity, Coherence.
Hill, A.B. (1965): The environment and disease: association or causation? Proceedings of the Royal Society of Medicine 58: 295-300  

Blinding: Masking participants in a research project from the expected outcome.  Blinding should at least be attempted with measurers (who are independent of the research and who do not know the study classifications assigned to subjects).  Blinding should also be attempted for therapists and subjects.

Control group: Subjects in an arm of an experimental research study who receive an intervention with an already known effect, against which the effect of the experimental intervention of interest is being assessed. Control groups can receive no intervention at all except time (charting the natural course of the disease) or interventions which have already known effects (ranging from minimal effects through to well-established quantifiable effects). 

Heterogeneity: Assessing whether samples are different in a specific measure, or in the way the samples respond to a specific situation. Usually this is tested statistically, and rejection of a null hypothesis is sought.

YouTube video: what is Heterogeneity? (Terry Shaneyfelt)
Systematic reviewers have to decide whether or not studies are homogeneous enough to combine. This video will describe what heterogeneity is and some of the tests used to investigate it.

 Homogeneity: Assessing whether samples are similar in a specific measure, or in the way the samples respond to a specific situation. Usually this is tested statistically, and evidence of a null hypothesis is sought.

Null hypothesis: The research framework that underpins most statistical tests for objective measures, particularly those used in experimental research. It is a way of thinking about treatment effects (start with the worst case scenario). The null hypothesis is a theoretical approach which seeks to establish no difference between interventions, or samples, and then tests for these using statistical approaches. If the statistical test provides a p value > 0.05, then the null hypothesis is accepted (i.e. there is no treatment effect, or no difference between groups). Conversely if the statistical test provides a p value < 0.05 then the null hypothesis is rejected (i.e. there is a difference between groups, or a treatment effect).

Power: Statistical power addresses the likelihood of a Type II error. Traditionally set at 1 - beta which beta  = 0.2 (0.8 or 80%). Randomisation: Randomisation is the unbiased (independent) selection of a research sample from the population of interest, and in the case of an experimental study, the allocation of subjects into treatment (intervention) arms. Randomisation reduces the potential for choice or chance to unduly influence the research outcomes.

Sample size calculation: The statistical method of determining how many people should be selected for a research study sample which has sufficient power to be robust and produce believable research findings. There are a number of good quality sample size calculators online. 

Categorical Data

Discrete groups/clusters of data within a sample.

Nominal categories:  Discrete data groups described only by words (male/ female).

Ordinal categories: Discrete data groups which can be described by words, and which also have a logical order (poor, moderate, good).

Categories assigned to interval data

Logical categories (clusters, groups) applied to equal interval data (e.g. people over 1.6 metres classified as 'tall'). This requires agreement on what constitutes the logical 'cut points' in the equal interval data for clusters/groups. 

Clinical Decision Rule

These are algorithms or scoring systems that lead to a prognostic estimation or a diagnostic category.

Clinical Prediction Rule

A weighted combination of measures used to predict whether an individual has a specific disease.

Confidence Interval

Range of values derived from a sample [mini-me of the population], within which the true population mean could be expected to lie. The sample should have been randomly selected and is appropriately sized (e.g. has a sample size calculation underpinning it). Confidence Intervals support comparisons between samples, and between samples and populations.
95% Confidence Intervals around the mean is commonly reported in research (expressing the range of values that has been derived from 95% of the individuals in the sample). On the assumption that the sample is normally distributed, 95%CI do not account for 2.5% of the sample in either tail.

95% CL = Mean +/- 1.96 x SE

1.96 is a value derived from z scores from a standardised normal distribution (mean 0, SD 1). 

Matching the range of outcomes obtained by 95% of a research sample (95%CI) (reported from experimental research) to real life settings (therapists' patients), provides therapists with an indication of the outcomes that could be anticipated in 95% of their patients, should their patients be similar to those in the research, and should the research intervention be applied in their practice setting.

In an experimental study which tests the effect of an intervention pre- and post, if the confidence interval around a treatment effect encompasses 0 [no difference], then at least some subjects obtained no effect of the intervention. In this instance, there will also be some subjects whose treatment effect will be negative, which may mean that the intervention made them worse (their pre- scores were better than their post-scores). In this situation, when reading this paper, you might not discount the usefulness of the intervention, as some of your patients may well benefit from it.  However you need sound information so that you can identify those patients who will do well, and those who may not do well.

In an observational study which tests cause and effect, if the confidence interval around a treatment effect encompasses 1 [no likely association], then at least some subjects will have no association between the cause and the effect. In this instance, there will also be some subjects with a protective relationship between cause and effect (where the OR or RR is less than 1), and others with a positive relationship between cause and effect (where the OR or RR is greater than 1). 

see this YouTube video on how to interpret and use confidence intervals (Terry Shaneyfelt)

Confounders

A confounding variable is associated with both exposure (cause) and disease (outcome). The confounder is not allowed to lie in the causal pathway between the cause and the outcome. Undetected and controlled-for confounding is a major threat to the validity of inferences made about cause and effect.

Adjusting for confounders: Statistical analysis that take the possible effect of confounders into account.

Adjusted Odds Ratio - Crude ORs that have been adjusted for the effect of a specific confounder, such that the association between Exposure and Disease now takes account of the influence of that confounder. 

Critical Appraisal

Aims to identify methodological flaws in the literature and provide consumers of research evidence the opportunity to make informed decisions about the quality of research evidence. It is the process of assessing and interpreting evidence by systematically considering its validity, results, and relevance.

Data Assessment

Where patients provide only one set of measures. This information is used for diagnosis, classification of risk factors, need for ongoing referral etc.

Differential diagnostic study

A study that looks at the ability to differentially diagnose a condition, i.e. our ability to differentiate between a number of different potential diagnoses, which present with similar signs and symptoms.

ECLIPSE

Format used to turn a clinical question into a workable search structure by breaking down the question into search terms. The appropriateness of the ECLIPSE-format will depend on the specific research question and on the type of studies that are suitable for addressing the review question. See also PECOT, PICO, PIPOH, or SPICE

Example Questions: How can the discharge procedure from the hospital to the community for people with head injuries be improved?

E (expectations):     about improvement or innovation or information

                                 e.g. improve the discharge procedure from the hospital to the community where rehabilitation will continue. What have other people
                                 done?

C (client group):      at who is the service aimed?

                                e.g. people with head injuries

L (location):            where is the service sited?

                                e.g. Community

I (Impact):               what is the change in the service which is being looked for? What would constitute success? How is this being measured? Similar to outcomes in
                                PICO-format

                                e.g. continuity of care; patient satisfaction; sense of communication between professionals

P (profession):        who delivered the service?

                                e.g. hospital nurses, community staff, social services

S (service):              type of service being investigated

                                 e.g. community rehabilitation service

Wildridge & Bell (2002) Health Info Libr J. 19(2): 113-5.

Economic and decision analyses

A study that uses explicit, quantitative methods (often based on economic outcomes) to analyse decisions (i.e. on intervention choices etc.) under conditions of uncertainty.

Effectiveness

Effectiveness is defined as 'the extent to which a treatment achieves its intended effect in the usual clinical setting'. It can be evaluated through observational studies of real practice. This allows practice to be assessed in qualitative as well as quantitative terms.

Haynes B. Can it work? Does it work? Is it worth it? Br Med J 1999;319:652-3.

Effect Modification

An interaction among multiple causes in a cause-and-effect relationship, where the estimate of the effect of one factor on a disease process depends on the presence of other study factors. An effect modifier on its own may not have a strong relationship with Disease, however in combination with other Exposures, may enhance or depress their associations.

Effect Size

Clinical ratio measure of the differences between treatment groups.  For interval/ratio data, effect size is calculated as (mean change in the control group - mean change in the intervention group) divided by the SD of the control group.  The larger the effect size, the larger the treatment effect.

Efficacy

Efficiency depends on whether a treatment is worth its cost to individuals or society. The most efficacious treatment, based on the best evidence, may not be the most cost-effective option.

Greenhalgh T. Is my practice evidence-based? Br Med J 1996;313:957-8.

Ecological Study

An epidemiological study in which the unit of analysis is a population rather than an individual. For instance, an ecological study may look at the association between smoking and lung cancer deaths in different countries. Not considered as strong as non-ecological designs such as cohort studies because it is susceptible to the ecological fallacy.

Evidence-Based Practice

Sackett et al (2000) provided one of the best known definitions of evidence-based practice (medicine) 'the conscientious, explicit and judicious use of current best evidence in making decisions about the care of individual patients'

 - Integration of best research evidence with clinical expertise and patient values

 - Optimises clinical health outcomes and recognises patient choices.

Sackett D et al (2000): Evidence-Based Medicine. Churchill Livingstone.

Factor Analysis

A statistical approach to find the most important combination of dependent variables to form an outcome measure, where there are multiple dependent variables.

Foreground Questions

Questions ask for specific knowledge to inform clinical decisions or actions.

Frequency Distribution

Graphical distribution of a measure taken from a sample, with the range of scores on the x axis and the N with each score on the y axis.

Normal frequency distribution: This distribution underpins most statistical tests.  It has two tails and a 'bell like' shape which describes data equally arranged around a central (most commonly occurring) point.

Abnormal frequency distribution: Can reflect many different shapes, and situations, where data is not equally arranged around a central point. Can include sigmoidal, exponential and multimodal distributions.

Gold Standard

The best available measure of a study variable (the measure incurring the least error).

Hierarchy of Evidence

A way of ranking the relative authority of various types of medical research.  There is no standard hierarchy of evidence, however there is agreement on the position in the hierarchy of different types of research. Secondary research (systematic reviews, meta-analyses) rank at the top of the hierarchy because they include data from multiple primary studies. Following this, experimental studies rank above observational studies because they attempt to control for bias in the research design.   Evidence-based on opinion is ranked lowest on the hierarchy. 

Intention To Treat Analysis (ITT)

Analysis which considers that everyone who begins the treatment is part of the trial, whether they finish it or not. The denominator is the total number of subjects who entered a trial, not the number who finished. ITT uses the last known value for each subject and carries this forward for subsequent analysis purposes. 

Inter-tester

Testing for consistency between researchers.

Interval Data Assigned To Categories

Interval scores assigned to ordinal categories for the purpose of analysing categories using equal interval statistics (poor, moderate, good scored as 1,2,3, even though the 'distance' between these categories may not be the same).

Interval Measures

Objective measures comprising equal predictable intervals, usually collected using a measurement instrument. Interval measures can be expressed in different forms, although all are equal. Some measures are discrete (cannot be broken into smaller segments e.g. number of patients in a ward, number of attempts at a task). Others are continuous as they can be broken into smaller and smaller segments, all of equal intervals (e.g. time [milliseconds, seconds, minutes etc.], height [centimetres, metres]).   

Intra-tester

Testing for consistency between the one researcher.

Mean (average)

Central tendency (middle value) in a normally distributed dataset. Calculated as the sum of the values in the dataset divided by the number of observations in the dataset.

Median

The statistic that is usually reported when a measure is not normally distributed (where the mean/average is not the central tendency). In this instance the median (50th%) is the central tendency, and is the number around which 50% of the values lie (above and below).

Minimal Clinically Significant (Important) Difference

'Minimal clinically important difference' as the smallest difference in a score in a domain of interest that patients perceive as beneficial and that would mandate, in the absence of side-effects and a change in the patient's management.

Jaeschke R, Singer J, Guyatt GH. Measurement of health status. Ascertaining the minimal clinically important difference. Control Clin Trials 1989;10:407-415

Mode

Most commonly occurring observation (measurement) in a dataset.

Multivariate Analysis

Where there are multiple independent variables which have an effect on the dependent variable.

Non-Parametric Data

Non-normally distributed data.

Number Need to Treat (NNT)

The number of patients who need to be treated with a new intervention in order to prevent one additional bad outcome (i.e. the number of patients that need to be treated for one to benefit compared with a control in a clinical trial). If NNT=1 then every patient provided with that intervention will benefit. 

Odds Ratio

An odds ratio (OR) is a measure of association between an exposure and an outcome. The OR represents the odds that an outcome will occur given a particular exposure, compared to the odds of the outcome occurring in the absence of that exposure.

OR = (odds of disease in exposed) / (odds of disease in the non-exposed)

 Outcome

The expected or looked for change in some measure or state, and are a result of an intervention.

Parametric Data

Normally distributed data

PECOT

Format used to turn a clinical question into a workable search structure by breaking down the question into search terms. The appropriateness of the PECOT-format will depend on the specific research question and on the type of studies that are suitable for addressing the review question. See also ECLIPSE, PICO, PIPOH, or SPICE.

Example question: Is acupuncture, compared with hypnosis, a successful intervention to use to stop teenagers smoking?

 P (population):       the demography of the population (age, gender, race)
                                the problem of the population (condition or diagnosis or symptoms)

                                e.g. teenagers

E (exposure):          who delivered the exposure (intervention/treatment)
                                how the exposure was delivered (frequency, dosage)
                                where the exposure was delivered (hospital, community centre)
                                what the exposure was (massage, splinting, exercises)

                                e.g. acupuncture

C (comparator):      comparison intervention (specific: weight bearing exercise)
                                alternative interventions (broad: any other treatment
                                control (nothing)

                                e.g. hypnosis

O (outcome):           change in symptoms of the population
                                 reason for using the exposure

                                e.g. smoking

T (time period):       short term
                                 long term
                                 not specified
                                 actual time specified (i.e. 6 months, 2 years)

                                e.g. short term

Percentage

Proportion multiplied by 100.  Thus if 6/10 of a sample was male, the percentage is 60%. 

Percentiles

Different percentile points in an abnormal distribution. Apart from the median (50th%), the most commonly reported percentiles are 25th% and 75th% (interquartile range). Growth charts and academic scores often use 10th% and 90th%, or 5th% and 95th%, as a determination of population variability.    

PICO

Format used to turn a clinical question into a workable search structure by breaking down the question into search terms. The appropriateness of the PICO-format will depend on the specific research question and on the type of studies that are suitable for addressing the review question. See also ECLIPSE, PECOT, PIPOH, or SPICE

Example Question: How well does a random urine protein to creatinine ratio diagnose proteinuria versus a 24-hour urine collection for protein?

P (population):        the demography of the population (age, gender, race)
                                the problem of the population (condition or diagnosis or symptoms)

                                e.g. people with diabetes

I (intervention):      what is the treatment under investigation

                                e.g. random urine protein to creatinine ratio

C (comparator):      comparison of intervention (specific: weight bearing exercise)
                                alternative interventions (broad: any other treatment)
                                control (nothing)

                                e.g. 24-hour urine collection for protein

O (outcome):           change in symptoms of the population
                                reason for using the exposure

                                e.g. diagnosis of proteinuria

PIPOH

Format used to turn a clinical question into a workable search structure by breaking down the question into search terms. The appropriateness of the PIPOH-format will depend on the specific research question and on the type of studies that are suitable for addressing the review question. See also ECLIPSE, PECOT, PICO or SPICE

P (population):        the demography of the population (age, gender, race)
                                the problem of the population (condition or diagnosis or symptoms)

I (intervention):      what is the treatment under investigation?

P (profession):        who delivered the intervention?

O (outcome):          change in symptoms of the population
                                reason for using the exposure

H (health care setting): where the intervention was delivered (hospital, community centre)

Primary Research Designs

Any research that is conducted on human subjects or animals (or parts thereof), and which therefore requires ethical approval.

Pilot study: A preliminary and small study to test elements of the planned larger study. It may examine processes and/ or outcomes. Indications of differences of effect are often used to inform the sample size calculations for the planned larger study. The findings of a pilot study should always be considered with caution, as the sample is rarely large enough to provide a definitive estimate of the effect of an intervention (experimental study), or of cause and effect (observational study).

Experimental research:  A planned attempt to assess the effects of a prospectively-delivered intervention. Interventions can also be called treatment(s), or manipulation of features of subjects' environments.

Group design: where a group of subjects is given the same intervention. The subjects are generally expected to be homogenous in the way they might respond to the intervention. Variability of study measures is established by the distribution of study data within the group.

N=1 (Single Case Experimental Design) (SCED): where one individual is given, at least, one intervention, and the effect of that intervention is compared with pre-intervention (baseline) measures. Within-subject variability is established by the distribution of data of repeated measures taken from that individual.

Intervention: what is done to subjects in an experimental study (also called treatment).

Pre-post study: where the one group of subjects is given the same intervention, and this group of subjects acts as its own controls. Outcome measures are taken before (pre, baseline), the intervention/treatment is delivered, and the outcome measures are taken again (post-intervention).

Quasi experimental study: two groups of subjects are compared, usually in different time periods or under different circumstances. One group usually is the 'control' in which no intervention occurs (usual care) and the second group receives the intervention. This is an experimental study approach to negotiate around situations where having a concurrent control/intervention study in the one organisation may result in violation of the study protocol (for instance where control subjects may end up receiving the intervention). This can occur when the control and intervention are being delivered concurrently in one hospital in two wards (one control, one intervention) and the control ward staff start delivering the intervention (or vice versa).

Cross-over trial: two groups of subjects receive both treatment and control interventions, but in different orders and time periods. This is a difficult study design to manage well, and the second arm of the study (the cross-over) is difficult to interpret in a systematic review/meta-analysis.

Clinical controlled trial (CCT, CT): an experimental study conducted in a known group of subjects in which all possible subjects are enrolled into the study.  Intervention arms are randomly assigned. This type of study usually occurs in clinical environments with defined access to study samples.

Randomised controlled trial (RCT): an experimental study conducted in a sample of subjects randomly selected from a known population.   Intervention arms are randomly assigned.

The table below considers the possibility of 'best practice' research design being applied to different experimental study types.

Comparison between experimental approaches

Differentiating between experimental and observational research reported from the one study. More than one paper can be written on the short (1) and long term (2) effects of an intervention. Short term outcomes reported from an intervention (experiment). Longer term outcomes (follow-up) reported where no further intervention is provided (observational), rather subjects are followed up.

Observational research: Research in which there is no deliberate intervention, and subject choices (and their effects) are observed and measured.

Case control study: where characteristics of a known group of subjects with a specific disease (cases) are compared with matched controls. Matching is usually by key confounding variables.

Cohort study: a group of subjects is recruited and their Disease and Exposure status is measured over time.

Audit: data collection from retrospectively collected patient notes or other files which record diagnoses, or treatment decisions.

Diagnostic studies: Studies which seek to improve diagnostic accuracy for specific conditions.

Mixed methods research: Where the study design incorporates features of different types of research in order to address the study question(s): experimental, observational and qualitative research.

Proportion

The portion of the whole (expressed as 1), that one element of a data category contributes.  E.g. if the data category is Gender, the elements are Male and Female, and the portion of the dataset that either Male or Female contributes is expressed as a decimal or a fraction. The portions of Males and Females in the sample should sum to 1. Thus if 6/10 of a sample was male, the proportion of men is 0.6.   Therefore in this instance the proportion of Females can be imputed as 0.4.

P value

The probability that a predetermined difference will be found in a study sample.

Traditional p value for determining significance is <0.05 (<5 in 100 occurrences/ chances).

Under the null hypothesis, if a p value >0.05 is found from statistical testing, there are more than 5 in 100 occurrences/chances that the difference between tests will be null.  So you will accept the null hypothesis that there is no difference between tests.

Under the null hypothesis, if a p value <0.05 is found from statistical testing, there are less than 5 in 100 occurrences/chances that the difference between tests will be null, so you will reject the null hypothesis, and find a significant difference between tests.

YouTube Video-P-Values and type 1 error (Terry Shaneyfelt)

Reference Standard

See Gold Standard

Relative risk

The ratio of the risk of disease/outcome among people who are exposed to the risk factor, to the risk among people who are unexposed. This is synonymous with risk ratio.

Reliability Study

Testing to quantify and minimise measurement error and demonstrate that one set of measures is a reasonable approximation of that sample of subjects' performance on repeated occasions of testing.

Sampling Error

Sampling error is the extent of how different a sample is from its reference population. It is inversely proportional to the square root of the sample size (1/ square root of N). The smaller the sample, the larger the sampling error.

Type 1 error: Claiming a treatment effect when none actually exists (alpha) (rejecting the null hypothesis when in fact the null hypothesis is correct).  Traditionally Type 1 error is set at 0.05 (5:100 chances of making such an error).  Making a treatment decision which is influenced by a Type 1 error means offering patients a treatment which is, in fact, ineffective.  

Type 2 error: Discounting a treatment effect when one actually exists (accepting the null hypothesis when in fact, it should be rejected). Traditionally Type 2 error is set at 0.20 (20:100 chances of making a Type 2 error) (beta). Making a treatment decision which is influenced by a Type 2 error means not offering patients a treatment which is, in fact, effective. 

Sampling Frames and Approaches

Method of selecting the sample (random, convenience etc.)

Random sampling: Using randomly-generated numbers (computer-generated numbers, dice, other independent (no interference) selection methods). This approach should produce an independent, unbiased mini-version of the population.

Consecutive sampling (usually used for diagnostic studies): Asking every consecutive person presenting at a particular venue for a specific reason (i.e. for confirmation of a diagnosis). Researchers should consider the availability of alternative locations where potential subjects might present, and equity and access to the selected location.

Convenience sampling: Asking all your friends, hand-picking people/organisations to participate, using volunteers. Researchers should consider 'comprehensiveness' and who might not be represented in sample (Who didn't you pick?, Who didn't you know was available for picking?, Characteristics of volunteers (who is likely, or not, to volunteer?)).

Systematic sampling: Asking every nth person, selecting systematically from best-available population 'register' (street directory of houses, people present in a particular location). Researchers should consider 'comprehensiveness' and who might not be captured (e.g. missing information in 'register', equity and access to selected location). This sampling approach can be flawed because unanticipated trends could influence the sampling frame.

Screening Intervention

Compares the implementation of the screening intervention in an asymptomatic population with a control group where the screening intervention is not employed or where a different screening intervention is employed. The aim is to see whether the screening intervention of interest results in improvements in patient-relevant outcomes e.g. survival.

Sensitivity

The capacity of a diagnostic test to identify an individual who has the disease of interest.

Specificity

The capacity of a diagnostic test to identify an individual who does not have the disease of interest.

SPICE

Format used to turn a clinical question into a workable search structure by breaking down the question into search terms. The appropriateness of the SPICE-format will depend on the specific research question and on the type of studies that are suitable for addressing the review question. See also ECLIPSE, PECOT, PICO or PIPOH

Example Question: what is the impact of an increase in the level of cost-sharing on access to health services for the chronically ill in European countries?

S (setting):              what is the context of the question?

                                e.g. European countries

P (perspective):      who are the users/potential users of the outcomes?

                                e.g. chronically ill

I (intervention):      what is being done to them?

                                e.g. increased cost-sharing

C (comparison):      what are the alternatives?

                                e.g. no increase

E (evaluation):        how will you measure if the intervention is successful?

                                           e.g. access to health services        

Standard Deviation

Approximates the 'average' spread of the data around the mean in one normally distributed sample (see below).

Standard Error of Mean

An approximation of the 'average' spread of the data around the mean in a population (i.e. minimising the influence of the number of observations in one sample).

SE = SD/ square root of N values in data set.

Standard Error of Measurement (SEM)

The standard error of measurement (SEM) is an estimate of error to use in interpreting an individual's test score. A test score is an estimate of a person's "true" test performance. Using a reliability coefficient and the test's standard deviation, we can calculate this value:

SEM  =  s  x square root of ( 1 - r)

Where:

     S = the standard deviation for the test    

      r = the reliability coefficient for the test

Study Directions

Data for research can be collected in a number of directions. Its trustworthiness is related to its currency.

Cross-sectional: When data is collected now with the intention of analysing it at this point-in-time. Generally trustworthy as data collection has a research intent.

Prospective:  When data is collected into the future. This is generally trustworthy as data collection has a research intent.

Retrospective:  When at least some of the data has already been collected and when there was no original intention to analyse that data. This is the least trustworthy data as it may be biased by undetected measurement or sampling errors.

Univariate Analysis

Where there is only one independent variable which is considered to have an effect on the dependent variable.

Validity Study

Testing that establishes the validity of a new measure, compared with a Gold Standard measure.

Variability

The spread of a measure in a sample (i.e. the distribution of height data in a research sample).

Term

Definition

Application Scholarship

Requires that researchers build bridges and collaborative relationships with other disciplines, decision and policy-makers and communities in order to apply theory to solve every-day problems. This iterative process by which knowledge is put into practice involves dynamic engagement and translation of new knowledge in practical interventions to solve real world problems experienced by individuals and society.

Source:

Boyer, E.L. (1990) Scholarship Reconsidered: Priorities of the Professoriate. Carnegie Foundation for the Advancement of Teaching, New York.

Capacity and Capacity Building

"In knowledge exchange, capacity is the set of skills, structures, and processes, as well as the organizational culture that allows, encourages, and rewards knowledge exchange. The Foundation works to build the capacity of decision-making and research organizations to achieve knowledge exchange in order to make decisions on the basis of research and other evidence."

Source:

Canadian Health Services Research Foundation (CHSRF) http://www.chsrf.ca/keys/glossary_e.php Retrieved October 25, 2010.

Communities of Practice

"Communities of practice are groups of people who share a concern or a passion for something they do and learn how to do it better as they interact regularly." These people don't necessarily work together every day, but they meet because they find value in their interactions. They typically share information, insight, and advice. They help each other solve problems. They discuss their situations, ponder common issues, explore ideas, and act as sounding boards. They may create tools, standards, generic designs, manuals, and other documents-or they may simply develop a tacit understanding that they share. They become bound by the value that they find in learning together.

Source:

Wenger, E. (1998). Communities of practice: learning, meaning, and identity. Cambridge University Press.

Conceptual Research Utilization

"Research findings from one or more studies that may change one's thinking but not necessarily one's particular or observable action."

Source: http://www.kusp.ualberta.ca/en/Resources/Glossary.aspx accessed 25 October 2010

Decision Maker

"Decision makers in the health services field can range from frontline health providers to administrators to ministers of health. However, the Foundation works with two particular groups of decision makers - managers and policy makers. These individuals often work in health services organizations such as hospitals and regional health authorities, as well as ministries of health and relevant regulatory agencies."

Source:

Canadian Health Services Research Foundation (CHSRF) http://www.cfhi-fcass.ca/PublicationsAndResources/ResourcesAndTools/GlossaryKnowledgeExchange.aspx Retrieved October 25, 2010. Updated June 30 2015

Deliberative or interactive model of Knowledge Translation

The deliberative, or interactive, model of knowledge translation promotes exchanges and cooperation between researchers, public health actors, members of civil society, and all other interested parties, throughout the research process. By promoting the co-production and co-interpretation of research, this model ensures the democratization of research knowledge and increases the likelihood of its being implemented.  

Sources:

Weiss, C. H. (1979). The many meanings of research utilization. Public Administration Review, September-October, 426-431.

Gauvin, Francois-Pierre (2010) Deliberative Processes and Knowledge Translation. National Collaborating Centre for Healthy Public Policy. Quebec, Canada http://www.ncchpp.ca

Deliberative Process

Deliberative processes are thinking processes about the way decisions are made. This process allows individuals to receive and exchange information, to critically examine an issue, and to negotiate an agreed position that can inform decision making.

Source:

Gauvin, Francois-Pierre (2010) Deliberative Processes and Knowledge Translation. National Collaborating Centre for Healthy Public Policy. Québec, Canada http://www.ncchpp.ca accessed October 18, 2010

Dissemination

CIHR (2010) suggests dissemination "involves identifying the appropriate audience and tailoring the message and medium to the audience. Dissemination activities can include such things as summaries for / briefings to stakeholders, educational sessions with patients, practitioners and/or policy makers, engaging knowledge users in developing and executing dissemination/implementation plan, tools creation, and media engagement." CHSRF provides the following definition, "Dissemination goes well beyond simply making research available through the traditional vehicles of journal publication and academic conference presentations. It involves a process of extracting the main messages or key implications derived from research results and communicating them to targeted groups of decision makers and other stakeholders in a way that encourages them to factor the research implications into their work. Face-to-face communication is encouraged whenever possible."

Sources:

CIHR (2010) More about Knowledge Translation. Ottawa, ON: Canadian Institutes of Health Research http://www.cihr-irsc.gc.ca/e/45321.html  Accessed Jan 12, 2016

Canadian Health Services Research Foundation (CHSRF) http://www.cfhi-fcass.ca/PublicationsAndResources/ResourcesAndTools/GlossaryKnowledgeExchange.aspx Retrieved October 25, 2010

Dissemination Strategy

"Is an evolving plan begun in advance of a research program that aims to:

 - extract clear, simple, and active main messages or key implications from research

    results;

 - identify credible "carriers" of the message; pinpoint key decision-maker audiences for

    the messages; and

 - develop ways to deliver messages that are appropriate to audiences being targeted

    and that encourage them to factor the research implications into their work.

Face-to-face communication is encouraged whenever possible."

Source:

Canadian Health Services Research Foundation (CHSRF) http://www.cfhi-fcass.ca/PublicationsAndResources/ResourcesAndTools/GlossaryKnowledgeExchange.aspx Retrieved October 25, 2010. Updated June 30, 2015.

Ethically-sound Application of Knowledge

"Ethically-sound KT activities for improved health are those that are consistent with ethical principles and norms, social values, as well as taking into account the complex and possibly competing legal and other regulatory frameworks. The term application is used to refer to the iterative process between people by which knowledge is put into practice."

Source:

CIHR (2010) More about Knowledge Translation. Ottawa, ON: Canadian Institutes of Health Research http://www.cihr-irsc.gc.ca/e/39033.html accessed Oct 18, 2010

Exchange

CIHR (2010) states, "The exchange of knowledge refers to the interaction between the knowledge user and the researcher, resulting in mutual learning. According to CHSRF, knowledge exchange is "collaborative problem-solving between researchers and decision makers that happens through linkage and exchange. Effective knowledge exchange involves interaction between knowledge users and researchers and results in mutual learning through the process of planning, producing, disseminating, and applying existing or new research in decision-making." Another definition is provided by CHSRF, "Linkage and exchange is the process of ongoing interaction, collaboration, and exchange of ideas between researcher and decision-maker communities. In research collaborations, it involves working together before, during, and after the research program."

Sources:

CIHR (2010) More about Knowledge Translation. Ottawa, ON: Canadian Institutes of Health Research http://www.cihr-irsc.gc.ca/e/39033.html accessed Oct 18, 2010

Canadian Health Services Research Foundation (CHSRF) http://www.chsrf.ca/keys/glossary_e.php Retrieved October 25, 2010

Implementation Science

"Scientific methods to promote the systematic uptake of clinical research findings and other evidence-based practices into routine practice and, hence, to improve the quality and effectiveness of health care."

Sources:

Foy, R., Eccles, M. & Grimshaw, J. (2001). Why does primary care need more implementation research? Family Practice 18:353-355. Graham, I.D., Logan, J., Harrison, M.B., Strauss, S.E., Tetroe, J., Caswell W. & Robinson, N. (2006). Lost in Knowledge Translation: Time for a Map? Journal of Continuing Education in the Health Professions 26(1): 13–24.

Individual Factors

"Those characteristics of the individual that influence the utilization of research by practitioners. Examples of characteristics thought to be relevant include: age, education level, autonomy, problem solving ability, open-mindedness, etc."

Source:

http://www.kusp.ualberta.ca/en/Resources/Glossary.aspx accessed 25 October 2010

Innovation

"Idea, practice, or object that is perceived as new by an individual or other unit of adoption."

Source:

Rogers EM. (1995) Diffusion of Innovations. New York: Free Press. p. 11.

Instrumental research utilization

"Concrete application of the research, which is normally translated into a material and usable form, such as a protocol or set of guidelines."

Source:

http://www.kusp.ualberta.ca/en/Resources/Glossary.aspx accessed 25 October 2010

Integration Scholarship

Is closely related to the interprofessional debates; it is about building connections across disciplines and shaping a more coherent and integrated use of knowledge. Integration work is creative connectedness and interpretation and synthesis, so is closely related to discovery, but poses somewhat different questions in terms of meaning and impact. Researchers locate their discovery work, or that of others, into broader intellectual patterns, thus moving beyond the disciplinary silos to build interdisciplinary partnerships with capacity to respond to multi-focal, complex human problems. Moreover, funding bodies are increasingly supportive of collaborative, integrated partnerships and teams as a way to generate knowledge and new KT approaches.

Source:

Boyer, E.L. (1990) Scholarship Reconsidered: Priorities of the Professoriate. Carnegie Foundation for the Advancement of Teaching, New York.

Knowledge Brokering

"Knowledge brokering links researchers and decision makers, facilitating their interaction so that they are able to better understand each other's goals and professional culture, influence each other's work, forge new partnerships, and use research-based evidence. Brokering is ultimately about supporting evidence-based decision-making in the organization, management, and delivery of health services. A knowledge broker is an individual or an organization that engages in knowledge brokering."

Source:

Canadian Health Services Research Foundation (CHSRF) http://www.cfhi-fcass.ca/PublicationsAndResources/ResourcesAndTools/GlossaryKnowledgeExchange.aspx Retrieved October 21, 2010. Updated June 30, 2015

Knowledge Transfer and Exchange

"A systematic approach to capture, collect and share tacit knowledge in order for it to become explicit knowledge. By doing so, this process allows for individuals and/or organizations to access and utilize essential information, which previously was known intrinsically to only one or a small group of people." Five key principles for KTE:

 1. What? Key messages must be clear, compelling ideas supported by a body of

      rigorous research.

 2. To Whom? The interaction should be specific to the audience.

 3. By Whom? The messenger must be considered credible by the audience

 4. How? Interactive engagement between the messenger and the audience is ideal

 5. With what effect? Performance measures must be audience specific and

     appropriate to the context.

Source:

Lavis, J. N., Robertson, D., Woodside, J. M., Mcleod, C. B., & Abelson, J. (2003). How can research organizations more effectively transfer research knowledge to decision makers? Milbank Quarterly, 81(2), 221-248.

Knowledge Translation

Knowledge translation (KT) refers to the assessment, review and implementation of scientific research evidence. KT goes beyond traditional dissemination - it is an ongoing iterative process of engagement which is the best predictor for seeing the findings applied (Lomas 2000). KT is about moving research into action - closing the gap between knowing and doing. KT is the science of methods to integrate and simplify knowledge into usable formats and of the barriers and enablers inherent the process for organizations and individuals. The focus is on the processes that affect how evidence is generated, communicated, and utilized as well as the barriers and enablers in different contexts. Researchers and stakeholders collaborate to identify and solve every-day problems. KT is vital because the creation of new knowledge does not (on its own) lead to implementation or impacts on health and secondly, we need to be accountable, so it is crucial to show the benefits of taxpayer dollars in health research by moving research into policy, programs and practice. But the mechanisms are not straightforward and must be tailored to the specific context. KT terminology abounds in the literature and on websites. For example, Graham et al (2006) identified 29 different related KT terms. This glossary includes a range of definitions and terms used by key global organisations and individuals in the field, however others may define the terms differently. Different KT approaches (e.g., end-of-grant KT; integrated KT) have differing inherent values about the process of knowledge production, so such premises may guide researchers in their selection. This glossary is not an exhaustive list, rather is a starting point for readers who wish to engage with the scientific field of knowledge translation.

Sources:

Lomas, J. (2000) Using 'Linkage and Exchange' to Move Research into Policy at a Canadian Foundation. Health Affairs 19(3): 236-40.

CIHR (2010) More about Knowledge Translation. Ottawa, ON: Canadian Institutes of Health Research http://www.cihr-irsc.gc.ca/e/39033.html  accessed Oct 25, 2010

Greenhalgh T, Robert G, Macfarlane F, Bate P & Kyriakidou O. (2004). Diffusions of innovations in service organizations: systematic review and recommendations. Milbank Quarterly 82(4):581-629.

Graham, I.D., J. Logan, M.B. Harrison, S.E. Strauss, J., Tetroe, W. Caswell & N. Robinson. (2006). Lost in Knowledge Translation: Time for a Map? Journal of Continuing Education in the Health Professions 26(1): 13-24.

Estabrooks, C.A., Thompson, D.S., Lovely, J. Jacque., E, & Hofmeyer, A. (2006) A Guide to Knowledge Translation Theory. Journal of Continuing Education in the Health Professions, 26(1): 25-36.

Lapaige, V. (2010) "Integrated knowledge translation" for globally oriented public health practitioners and scientists: Framing together a sustainable transfrontier knowledge translation vision. Journal of Multidisciplinary Healthcare, 3:33-47.

Knowledge Translation Definition - CHSRF

Knowledge translation and exchange (KTE) - formerly knowledge transfer: "Knowledge exchange is collaborative problem-solving between researchers and decision makers that happens through linkage and exchange. Effective knowledge exchange involves interaction between decision makers and researchers and results in mutual learning through the process of planning, producing, disseminating, and applying existing or new research in decision-making."

Source:

Canadian Health Services Research Foundation (CHSRF) http://www.cfhi-fcass.ca/PublicationsAndResources/ResourcesAndTools/GlossaryKnowledgeExchange.aspx Retrieved October 25, 2010. Updated June 30, 2015.

Knowledge Translation Definition - CIHR

Knowledge translation is "a dynamic and iterative process that includes synthesis, dissemination, exchange and ethically sound application of knowledge to improve the health of Canadians, provide more effective health services and products and strengthen the health care system."

Sources:

CIHR (2004) Knowledge translation strategy 2004 - 2009: Innovation in action. Ottawa, ON: Canadian Institutes of Health Research. http://www.cihr-irsc.gc.ca/e/29418.html

Tetroe, J. (2007). Knowledge Translation at the Canadian Institutes of Health Research: A Primer. Focus Technical Brief No. 18. Austin, TX: National Center for the Dissemination of Disability Research. Retrieved October 25, 2010. http://www.ncddr.org/kt/products/focus/focus18/

Knowledge Translation (End-of-Grant)

Covers the diffusion, dissemination and application of knowledge that researchers undertake once the findings from a project are available. A plan is implemented for making users aware of the knowledge that was gained in the project, so includes typical dissemination and communication activities such as publications in peer-reviewed journals and conference presentations. End-of-grant KT can also involve messages that are tailored to specific audiences such as summary briefing notes for stakeholders, interactive educational sessions with patients, practitioners and policy-makers, media or using knowledge brokers.

Source:

CIHR (2010) Knowledge To Action: An end of grant knowledge translation casebook. More about Knowledge Translation. Ottawa, ON: Canadian Institutes of Health Research http://www.cihr-irsc.gc.ca/e/documents/cihr_kt_casebook_2010_e.pdf        accessed October 25, 2010

Knowledge Translation (Integrated)

A way of doing research that involves decision makers/knowledge-users - usually as members of the research team - in all stages of the research process. CIHR explains, "In integrated KT, stakeholders or potential research knowledge users are engaged in the entire research process. By doing integrated KT, researchers and research users work together to shape the research process by collaborating to determine the research questions, deciding on the methodology, being involved in data collection and tools development, interpreting the findings, and helping disseminate the research results. This approach, also known by such terms as collaborative research, action-oriented research, and co-production of knowledge, should produce research findings that are more likely be relevant to and used by the end users." Lapaige (2010, p. 34) argues that "integrated KT refers to both a process and its result."

Sources:

CIHR (2010) More about Knowledge Translation. Ottawa, ON: Canadian Institutes of Health Research http://www.cihr-irsc.gc.ca/e/39033.html  accessed Oct 18, 2010

Lapaige, V. (2010) “Integrated knowledge translation” for globally oriented public health practitioners and scientists: Framing together a sustainable transfrontier knowledge translation vision. Journal of Multidisciplinary Healthcare, 3:33-47.

Knowledge Translation: new term to describe an old problem

Knowledge translation is a new term to describe a problem identified decades ago, specifically the haphazard uptake and the underutilization of evidence-based research which has been described as the gap between what is "known" and "what is currently done" in practice.

Sources:

Davis, D., Evans, M., Jadad, A., Perrier, L., Rath, D., Ryan, D., et al. (2003). The case for knowledge translation: Shortening the journey from evidence to effect. British Medical Journal, 327(7405), 33-35.

Grol, R., & Grimshaw, J. (2003). From best evidence to best practice: Effective implementation of change in patients' care. Lancet, 362(9391), 1225-1230.

Schuster, M.A., McGlynn, E.A., Brook, R.H. (1998). How Good Is the Quality of Health Care in the United States? Milbank Quarterly 76:517-64.

Grol, R. (2000). Twenty years of implementation research. Family Practice, 17, S32-S35.

Knowledge User

We are all users of knowledge in our daily lives. In the healthcare context, a knowledge user is a person:

 - who is likely to be able to use the knowledge generated through research in order to

    make informed decisions about health policies, programs and/or practices;

 - whose level of engagement in the research process may vary in intensity and

   complexity depending on the nature of the research and their information needs; 

 - who can be, but is not limited to, a practitioner, policy-maker, educator, decision-

   maker, health care administrator, community leader, or an individual in a health

   charity, patient group, private sector organization or a media outlet.

Source:

CIHR (2010) More about Knowledge Translation. Ottawa, ON: Canadian Institutes of Health Research http://www.cihr-irsc.gc.ca/e/39033.html  accessed Oct 18, 2010

Knowledge-to-Action Process

The Knowledge to Action Process conceptualizes the relationship between knowledge creation and action. The action part of the process can be thought of as a cycle leading to implementation or application of knowledge. Access this diagram at the following link.

Source:

CIHR (2010) More about Knowledge Translation. Ottawa, ON: Canadian Institutes of Health Research http://www.cihr-irsc.gc.ca/e/39033.html  accessed Oct 18, 2010

Meta-Analysis

"Systematic review that uses quantitative methods to synthesize and summarize results."

Source:

Straus, SE., Richardson, WS., Glasziou, P. & Haynes, R (2005) Evidence-Based Medicine: How to Practice and Teach EBM, 3rd ed Elsevier Churchill Livingstone pg. 281

Meta-Synthesis

"Entails a comparison, translation, and analysis of original findings from which new interpretations are generated, encompassing and distilling the meanings in the constituent studies."

Source:

Zimmer, L. (2006) Qualitative meta-synthesis: a question of dialoguing with text. Journal of Advanced Nursing 53(3), 311-318

Mode I

Mode I knowledge production reflects the traditional, academic norms of scholarship in the disciplines and institutions which researchers work. Academic tenure and promotion are based on high impact, peer-reviewed publication. It is a mode of knowledge production whose foundations rest on principles of scientific expertise, peer review, and non-interference. Mode I activities are consistent with Boyer's scholarship of discovery that generates new knowledge or challenges current knowledge in a discipline.

Sources:

Gibbons, M., Limoges, C., Nowotny, H., Schwartzman, S., Scott, P. (1994). The New Production of Knowledge. Sage, London.

Nowotny, H., Scott, P., Gibbons, M. (2001). Re-thinking Science: Knowledge and the Public in an Age of Uncertainty. Polity Press, Cambridge, MA.

Boyer, E.L. (1990). Scholarship Reconsidered: Priorities of the Professoriate. Carnegie Foundation for the Advancement of Teaching, New York.

Mode II

Mode II knowledge production activities involves building meaningful and sustainable relationships with end users non-hierarchical relationships with research end-users known as stakeholders (e.g., industry, government policy-makers, health care decision-makers) to collaborate on a research issue situated in a specific health care context. In this sense, Mode II knowledge production is based on the needs of end users in the healthcare system. Lapaige (2010, p. 36) argues that "Mode II is based on the assumption that science can no longer be confined to the university....Mode II contrasts with Mode I of knowledge production whose substance was science confined within disciplinary boundaries. A Mode II perspective argues for co-operation by researchers for resolving critical problems in a changing global context."

Sources:

Nowotny, H., Scott, P., Gibbons, M., 2001. Re-thinking Science: Knowledge and the Public in an Age of Uncertainty. Polity Press, Cambridge, MA.

Lapaige, V. (2010) "Integrated knowledge translation" for globally oriented public health practitioners and scientists: Framing together a sustainable transfrontier knowledge translation vision. Journal of Multidisciplinary Healthcare, 3:33-47.

Organizational Factors

"Those factors or characteristics of the organization that influence the diffusion of innovations or the utilization of research by practitioners (e.g. administrative support, access to research, size, complexity, staffing, organizational culture, etc.)."

Source:

http://www.kusp.ualberta.ca/en/Resources/Glossary.aspx accessed 25 October 2010

Quality of Care

"The degree to which health services for individuals and populations increase the likelihood of desired health outcomes and are consistent with current professional knowledge".

Source:

IOM (1990). Medicare: A Strategy for Quality Assurance, Volume I. Institute of Medicine, Washington, DC: National Academy Press. page 21 http://www.nap.edu/catalog.php?record_id=1547

Research Utilization

"Specific kind of knowledge utilization whereby the knowledge has a research base to substantiate it. It is a complex process in which knowledge, in the form of research, is transformed from the findings of one or more studies into instrumental, conceptual, or persuasive utilization."

Source:

http://www.kusp.ualberta.ca/en/Resources/Glossary.aspx accessed 25 October 2010

Social Capital

Social capital is defined as norms (cooperation, trust, communication) and networks (bonding, bridging, linking) that enable people to act collectively and share resources (knowledge, favours information) for productive purposes (Woolcock & Narayan 2000). Social capital adds value to individuals and groups. Through informal connections that individuals make in their networks (e.g., community of practice) and in the formal process of sharing their expertise, learning from others, and participating in the group, members are said to be acquiring social capital or a trust that members build between themselves and others that can lead to better communication and then action.

Sources:

Woolcock, M. & Narayan (2000) Social Capital: Implications for Development Theory, Research, and Policy. World Bank Research Observer, 15, 225-250.

Wenger, E. (1998). Communities of practice: learning, meaning, and identity. Cambridge University Press.

Summary

"Summaries are a less formal way of pulling research together, generally using a more conversational [plain language] tone. Where a formal synthesis can be considered to be the creation of new knowledge, a summary clearly pulls together main messages from a number of published sources."

Source:

Canadian Health Services Research Foundation (CHSRF) http://www.cfhi-fcass.ca/PublicationsAndResources/ResourcesAndTools/GlossaryKnowledgeExchange.aspx Retrieved October 25, 2010. Updated June 30, 2015

Synthesis

CIHR (2010) states, "Synthesis, in this context, means the contextualization and integration of research findings of individual research studies within the larger body of knowledge on the topic. A synthesis must be reproducible and transparent in its methods, using quantitative and/or qualitative methods. It could take the form of a systematic review, follow the methods developed by the Cochrane Collaboration, result from a consensus conference or expert panel or synthesize qualitative or quantitative results. Realist syntheses, narrative syntheses, meta-analyses, meta-syntheses and practice guidelines are all forms of synthesis." Resources related to synthesis are available.  Another definition of synthesis is provided by CHSRF as, "an evaluation or analysis of research evidence and expert opinion on a specific topic to aid in decision-making or help decision makers in the development of policies. It can help place the results of a single study in context by providing the overall body of research evidence. There are many forms of synthesis, ranging from very formal systematic reviews, like those carried out by the Cochrane Collaboration, to informal literature reviews. The Foundation conducts syntheses aimed at making "best practice" recommendations for a specific area of management or policy development."

Sources:

CIHR (2010) More about Knowledge Translation. Ottawa, ON: Canadian Institutes of Health Research http://www.cihr-irsc.gc.ca/e/39033.html

Canadian Health Services Research Foundation (CHSRF) http://www.cfhi-fcass.ca/PublicationsAndResources/ResourcesAndTools/GlossaryKnowledgeExchange.aspx Retrieved October 25, 2010. Updated June 30, 2015

Utilization

"Focused on assisting with the actual adoption process after dissemination and diffusion have occurred. When the term utilization is used in the context of "research utilization", it usually refers to a complex problem solving, critical thinking, and decision-making process undertaken by clinicians and not just the use of research in an instrumental way."

Source:

http://www.kusp.ualberta.ca/en/Resources/Glossary.aspx accessed 25 October 2010