Patient and public engagement in research and health system decision making: A systematic review of evaluation tools
Bibliographic record
Abstract
BACKGROUND: Patient and public engagement is growing, but evaluative efforts remain limited. Reviews looking at evaluation tools for patient engagement in individual decision making do exist, but no similar articles in research and health systems have been published. OBJECTIVE: Systematically review and appraise evaluation tools for patient and public engagement in research and health system decision making. METHODS: We searched literature published between January 1980 and February 2016. Electronic databases (Ovid MEDLINE, Embase, Cochrane Database of Systematic Reviews, CINAHL and PsycINFO) were consulted, as well as grey literature obtained through Google, subject-matter experts, social media and engagement organization websites. Two independent reviewers appraised the evaluation tools based on 4 assessment criteria: scientific rigour, patient and public perspective, comprehensiveness and usability. RESULTS: In total, 10 663 unique references were identified, 27 were included. Most of these tools were developed in the last decade and were designed to support improvement of engagement activities. Only 11% of tools were explicitly based on a literature review, and just 7% were tested for reliability. Patients and members of the public were involved in designing 56% of the tools, mainly in the piloting stage, and 18.5% of tools were designed to report evaluation results to patients and the public. CONCLUSION: A growing number of evaluation tools are available to support patient and public engagement in research and health system decision making. However, the scientific rigour with which such evaluation tools are developed could be improved, as well as the level of patient and public engagement in their design and reporting.
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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 | 0.257 | 0.510 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.014 | 0.016 |
| Bibliometrics | 0.040 | 0.034 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.011 | 0.011 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".