Advancing Patient Engagement in Health Service Improvement: What Can the Evaluation Community Offer?
Bibliographic record
Abstract
Abstract: Despite efforts for greater patient engagement in health care quality improvement, evaluation practice in this context remains mostly conventional and noncollaborative. Following an explication of this problem we discuss relevant theory and research on patient-centred care (PCC) and patient engagement and then consider potential benefits of collaborative and participatory approaches to evaluation of such initiatives. We argue that collaborative approaches to evaluation (CAE) are logically well-suited to the evaluation of PCC initiatives and then suggest contributions that the evaluation community can offer to help advance patient engagement. Finally, we outline a research agenda that identifies important areas that are in need of further examination.
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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 | 0.661 | 0.616 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.009 | 0.004 |
| Bibliometrics | 0.010 | 0.008 |
| Science and technology studies | 0.024 | 0.041 |
| Scholarly communication | 0.062 | 0.053 |
| Open science | 0.009 | 0.048 |
| Research integrity | 0.030 | 0.023 |
| Insufficient payload (model declined to judge) | 0.009 | 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".