Developing a program theory of patient engagement in patient-oriented research and the impacts on the health care system: protocol for a rapid realist review
Bibliographic record
Abstract
BACKGROUND: The patient-oriented research (POR) discourse has been criticized as being fragmented, lacking consistent terminology and having few evaluative studies. Our research team will use rapid realist review methodology to generate broad, process-based program theory regarding how partnering patients with researchers in POR generates an impact within a health care system. METHODS: This protocol for a rapid realist review will involve multiple steps, including research question development; preliminary program theory and search strategy development; study selection and appraisal; data extraction, analysis and synthesis; and program theory refinement. We will be guided by the Realist and Meta-narrative Evidence Syntheses: Evolving Standards (RAMESES) publication standards for realist synthesis. Unlike traditional reviews, a realist review aims to discover and understand causal processes that exist within a complex environment, asking questions regarding what works for whom, under what circumstances, how and why. Our multidisciplinary team consists of patient partners, health care professionals, a health sciences librarian and health services researchers. Patient partners are full research partners, supporting development of our guiding research question and identifying community partners and stakeholder groups to disseminate our findings. Patient partners will be asked to recommend literature sources, to review and vet our set of search terms, and to review, evaluate and reflect on our initial program theory in light of their personal, lived expertise. INTERPRETATION: We will share the results of our rapid realist review with community partners and stakeholder groups. We will also disseminate our program theory by means of publication in a peer-reviewed journal and presentation at scientific conferences.
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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 | 0.280 | 0.453 |
| Meta-epidemiology (narrow) | 0.007 | 0.008 |
| Meta-epidemiology (broad) | 0.012 | 0.015 |
| Bibliometrics | 0.020 | 0.020 |
| Science and technology studies | 0.007 | 0.010 |
| Scholarly communication | 0.016 | 0.013 |
| Open science | 0.009 | 0.010 |
| Research integrity | 0.012 | 0.015 |
| Insufficient payload (model declined to judge) | 0.090 | 0.025 |
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".