Towards an assessment for organizational participatory research health partnerships: A systematic mixed studies review with framework synthesis
Bibliographic record
Abstract
Within the health sciences, organizational participatory research (OPR) is defined as a blend of research and action, in which academic researchers partner with health organization members. OPR is based on a sound partnership between all stakeholders to improve organizational practices. However, little research on the evaluation of OPR health partnership exists. This systematic mixed studies review sought to produce a new theoretical model that structures the evaluation of the OPR processes and related outcomes of OPR health partnerships. Six bibliographic databases were searched together with grey literature sources for OPR health partnership evaluation questionnaires. Six questionnaires were included, from which a pool of 95 OPR health partnership evaluation items were derived. The included questionnaires were appraised for the quality of their origin, development and measurement properties. A framework synthesis was performed using an existing OPR framework by organizing questionnaire items in a matrix using a hybrid thematic analysis. This led to our proposed Organizational Participatory Research Evaluation Model (OPREM) that includes three axes, Trust, Collective Learning and Sustainability (with specific dimensions) and 95 items. This model provides information to help stakeholders comprehensively structure the evaluation of their partnerships and subsequent improvement; thus, potentially helping to improve health organization practices.
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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 | 0.290 | 0.354 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.011 | 0.012 |
| Bibliometrics | 0.050 | 0.034 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.014 | 0.015 |
| Open science | 0.006 | 0.013 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".