Patient/public perceptions on engagement with a medical school: What needs to happen to support authentic and sustained participation
Bibliographic record
Abstract
PURPOSE: Patient/public involvement in health professional education is increasing but remains episodic, narrowly focused, reliant on individual enthusiasts, and lacks supportive institutional infrastructure. There is little evidence-informed practical guidance on how to take a more strategic and formal approach. We undertook a qualitative study to learn from patients and the public how medical schools could engage in an authentic and sustainable way. METHODS: In 2022 we conducted eight focus groups with patients and members of community organizations. Participants were asked about experiences and perceptions of what needs to happen to enable and support them to participate in medical education, barriers to authentic engagement, and how they might be overcome. Recordings were transcribed and data coded inductively. A summary report was circulated to participants for validation of findings. RESULTS: The focus groups were attended by 38 participants representing a wide variety of perspectives. Participants provided practical suggestions that we categorized into six major themes: inviting participation; preparing for participation; supporting participation; increasing and supporting diversity; recognizing participation; institutional buy-in and support. CONCLUSIONS: Individual instructors can enhance authentic patient engagement through recruitment, support and recognition practices. Institutional commitment is required to sustain and widen participation through funding, policies and infrastructure.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.025 | 0.041 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.010 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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".