A protocol for a systematic review of the use of process evaluations in knowledge translation research
Bibliographic record
Abstract
BACKGROUND: Experimental designs for evaluating knowledge translation (KT) interventions for professional behavior change can provide strong estimates of intervention effectiveness but offer limited insight how the intervention worked or not. Furthermore, trials provide little insight into the ways through which interventions lead to behavior change and how they are moderated by different facilitators and barriers. As a result, the ability to generalize the findings from one study to a different context, organization, or clinical problem is severely compromised. Consequently, researchers have started to explore the causal mechanisms in complementary studies (process evaluations) alongside experimental designs for evaluating KT interventions. This study focuses on improving process evaluations by synthesizing current evidence on process evaluations conducted alongside experimental designs for evaluating KT interventions. METHODS/DESIGN: A medical research librarian will develop and implement search strategies designed to identify evidence that is relevant to process evaluations in health research. Studies will not be excluded based on design. Included studies must contain a process evaluation component aimed at understanding or evaluating a KT intervention targeting professional behavior change. Two reviewers will perform study selection, quality assessment, and data extraction using standard forms. Disagreements will be resolved through discussion or third party adjudication. Data to be collected include study design, details about data collection approaches and types, theoretical influences, approaches to evaluate intervention dose delivered, intervention dose received, intervention fidelity, intervention reach, data analysis, and study outcomes. This study is not registered with PROSPERO. DISCUSSION: There is widespread acceptance that the generalizability of quantitative trials of KT interventions would be significantly enhanced to other contexts, health professional groups, and clinical conditions through complementary process evaluations alongside trials. This systematic review will serve as a 'state of the science' on methodological approaches to process evaluations and will allow us to: 1) take stock of current research approaches and 2) develop concrete recommendations for knowledge users (e.g., quality consultants and health services researchers) designing future KT process evaluations.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.214 | 0.313 |
| Meta-epidemiology (narrow) | 0.007 | 0.007 |
| Meta-epidemiology (broad) | 0.018 | 0.017 |
| Bibliometrics | 0.022 | 0.026 |
| Science and technology studies | 0.008 | 0.008 |
| Scholarly communication | 0.012 | 0.013 |
| Open science | 0.009 | 0.008 |
| Research integrity | 0.014 | 0.015 |
| Insufficient payload (model declined to judge) | 0.127 | 0.028 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".