Patient involvement in medical education: To what problem is engagement the solution?
Bibliographic record
Abstract
CONTEXT: Patient and public engagement is gaining momentum across many domains of health care, inclusive of education and research. In this framing, engagement is offered as a solution to a myriad of problems. Yet, the way problems and solutions are linked together may be assumed, rather than made explicit. In the absence of clarity, there is a risk that solutions that may have worked in one domain of health care could falter, or even create new problems, in another. METHODS: We use a model from organisational studies as a way to make sense of the relationships between the problems, solutions and stakeholders operating in the name of patient and public engagement in health care. The 'garbage can model' is a playfully phrased but meaningful attempt to decipher the complex world of decision making in organisations. We use this model to guide our framing of the solutions of patient engagement practice and the wide range of problem statements that animate all of this activity. RESULTS: Following a discussion of the complexity of the field of patient engagement, we identify strategies for educators to conceptually weave problem statements, solutions and stakeholders together in mosaics of engagement activity. We further suggest a movement away from considering problems to be solved to thinking about polarities to be navigated. CONCLUSIONS: As patient engagement becomes more embedded in decision-making spaces in health professions education, we need a better understanding of how decisions are actually made in these organisations. We also need to consider that our most treasured solutions may have an uneasy fit, and some unintended consequences, as they enter new domains of health care. Finally, we advocate for critical approaches not just to the solutions of patient engagement, but to understand problem statements as they are defined, upheld and disrupted through all of this work.
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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 | 0.105 | 0.150 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.021 | 0.086 |
| Scholarly communication | 0.040 | 0.056 |
| Open science | 0.006 | 0.041 |
| Research integrity | 0.031 | 0.040 |
| Insufficient payload (model declined to judge) | 0.009 | 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; 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".