“Part of the Team”: Mapping the outcomes of training patients for new roles in health research and planning
Bibliographic record
Abstract
BACKGROUND: to find new ways to engage patients in a new interdisciplinary organization to support evidence-informed improvements in clinical outcomes across the health system. OBJECTIVE: Implement and test a new research method and training curriculum to build patient capacity for engagement in health through peer-to-peer research. DESIGN: Programme evaluation using Outcome Mapping and the grounded theory method. SETTING AND PARTICIPANTS: Twenty-one patients with various chronic conditions completed one year of training in adapted qualitative research methods, including an internship where they designed and conducted five peer-to-peer inquiries into a range of health experiences. MAIN OUTCOME MEASURES: Outcomes were continually monitored and evaluated using an Outcome Mapping framework, in combination with grounded theory analysis, based on data from focus groups, observation, documentation review and semi-structured interviews (21 patient researchers, 15 professional collaborators). RESULTS: Key stakeholders indicated the increased capacity of patients to engage in health-care research and planning, and the introduction and acceptance of new, collaborative roles for patients in health research. The uptake of new patient roles in health-care planning began to impact attitudes and practices. CONCLUSIONS: Patient researchers become "part of the team" through cultural and relationship changes that occur in two convergent directions: (i) building the capacity of patients to engage confidently in a dialogue with clinicians and decision makers, and (ii) increasing the readiness for patient engagement uptake within targeted organizations.
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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 | 0.054 | 0.118 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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; 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".