A scoping review of methods to measure and evaluate citizen engagement in health research
Bibliographic record
Abstract
Abstract Background Citizen engagement, or partnering with interested members of the public in health research, is becoming more common. While ongoing assessment of citizen engagement practices is considered important to its success, there is little clarity around aspects of citizen engagement that are important to assess (i.e., what to look for) and methods to assess (i.e., how to measure and/ or evaluate) citizen engagement in health research. Methods In this scoping review, we included peer-reviewed literature that focused primarily on method(s) to measure and/or evaluate citizen engagement in health research. Independently and in duplicate, we completed title and abstract screening and full-text screening and extracted data including document characteristics, citizen engagement definitions and goals, and methods to measure or evaluate citizen engagement (including characteristics of these methods). Results Our search yielded 16,762 records of which 33 records (31 peer-reviewed articles, one government report, one conference proceeding) met our inclusion criteria. Studies discussed engaging citizens (i.e., patients [n = 16], members of the public [n = 7], service users/consumers [n = 4], individuals from specific disease groups [n = 3]) in research processes. Reported methods of citizen engagement measurement and evaluation included frameworks, discussion-based methods (i.e., focus groups, interviews), survey-based methods (e.g., audits, questionnaires), and other methods (e.g., observation, prioritization tasks). Methods to measure and evaluate citizen engagement commonly focused on collecting perceptions of citizens and researchers on aspects of citizen engagement including empowerment, impact, respect, support, and value. Discussion and conclusion We found that methods to measure and/or evaluate citizen engagement in health research vary widely but share some similarities in aspect of citizen engagement considered important to measure or evaluate. These aspects could be used to devise a more standardized, modifiable, and widely applicable framework for measuring and evaluating citizen engagement in research. Patient or public contribution Two citizen team members were involved as equal partners in study design and interpretation of its findings. Systematic review registration Open Science Framework (10.17605/OSF.IO/HZCBR).
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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 | 0.276 | 0.594 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.012 | 0.012 |
| Bibliometrics | 0.084 | 0.071 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.016 | 0.014 |
| Open science | 0.008 | 0.012 |
| Research integrity | 0.008 | 0.006 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".