Patients as experts in the illness experience: Implications for the ethics of patient involvement in health professions education
Bibliographic record
Abstract
In response to calls to increase patient involvement in health professions education (HPE), educators are inviting patients to play a range of roles in the teaching of clinical trainees. However, there are concerns that patients involved in educational programs are seen as representing a demographic larger than themselves: their disease, their social group or even patients as a whole. This leads to difficult ethical challenges related to representation, including problems of tokenistic inclusion and of inadvertently essentializing marginalized groups. We propose that conceptualizing patients as experts in their illness experience can help resolve these dilemmas of representation equitably and effectively. Just as clinical experts are involved in HPE to share their expertise and represent their clinical experience, so too should patients be invited to participate in HPE explicitly for their expertise in their illness experience. This framing clarifies the goals of patient involvement as technocratic rather than tokenistic, mandates meaningful contributions by patients, and helps frame patient involvement for learners as the presentation of expert perspectives.
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 | 0.096 | 0.090 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.019 | 0.100 |
| Scholarly communication | 0.018 | 0.021 |
| Open science | 0.004 | 0.024 |
| Research integrity | 0.019 | 0.021 |
| Insufficient payload (model declined to judge) | 0.004 | 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".