Patient and public engagement in research and health system decision making: A systematic review of evaluation tools
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.021 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it