Dilemmas of Representation: Patient Engagement in Health Professions Education
Bibliographic record
Abstract
The role of the patient in bedside teaching has long been a matter of consideration in health professions education. Recent iterations of patient engagement include patients as storytellers, members of curriculum planning committees, guest lecturers, and health mentors. While these forms of patient engagement are reported to have many benefits for learners, educators, and the patients themselves, there is concern that such programs may not be representative of the diversity of patients that health care professionals will encounter throughout their careers. This problem of representation has vexed not only educators but also sociologists and political scientists studying patients' and the public's involvement in arenas such as health services research, policy, and organizational design.In this Perspective, the authors build on these sociological and political science approaches to expand our understanding of the problem of representation in patient engage-ment. In doing so, the authors' reconfiguration of the problem sheds new light on the dilemma of representation. They argue for an understanding of representation that not only is inclusive of who is being represented but that also takes seriously what is being represented, how, and why. This argument has implications for educators, learners, administrators, and patient participants.
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 | 0.091 | 0.115 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.033 | 0.115 |
| Scholarly communication | 0.030 | 0.031 |
| Open science | 0.004 | 0.028 |
| Research integrity | 0.026 | 0.026 |
| Insufficient payload (model declined to judge) | 0.005 | 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".