Patients’ Voices Are Important in Compassion Education
Bibliographic record
Abstract
To the Editor: I read with interest Sinclair and colleagues’ review 1 of compassion education in health care. It was clear from their analyses that the quality of research in this field is underwhelming thus far. I was particularly struck by the paucity of studies that incorporated patients’ feedback as an educational strategy or outcome measure. Surely, understanding patients’ experiences is an intrinsic part of developing compassion. Therefore, should not patients have more of a say in how well we are doing? There are several reasons that feedback from patients should play a prominent role throughout medical training. First, there is research to suggest that positive online ratings of physicians by patients correlate with a decreased likelihood of disciplinary convictions of physicians—although the role of such evaluations of clinical performance remains controversial. 2 As online reviews are becoming more commonplace, it would be valuable for trainees to understand how patients may perceive them before they enter independent practice. Second, it is recognized that there is an “erosion of empathy” that occurs during the course of medical training. 3 Inclusion of patient-based evaluations may help reinforce the lessons of compassion education as trainees become more independent and their patient loads increase. Last, the COVID-19 pandemic has made health care delivery more distanced and patients more isolated. The ability to elicit and understand patients’ needs has thus become even more important, and this requires direct patient input. Health care itself has become more patient-centered, and it behooves us to include patients both in medical education and in the research concerning it. More medical education programs are moving toward competency-based models, and this provides an opportunity to integrate patients’ feedback as a means to evaluate trainees’ competencies like interpersonal skills and compassion. After all, health care is fundamentally a service industry, and it is time we gave more of a voice to the individuals we are serving.
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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 | 0.006 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.017 | 0.021 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".