Bibliographic record
Abstract
We thank Naik for their comments on our review and agree that the voices of patients—the recipients of compassion—are strikingly absent in the evaluation of current and future physicians’ clinical competencies, including their competency in compassion. Naik provides compelling reasons for why patients’ feedback should be more prominent in medical education. So why, in a world of patient-orientated research and patient-centered care, has integrating patients’ feedback into medical and continuing medical education, including patients’ reported experiences of compassion, remained a persistent challenge? While progress has been made in competency-based medical education, learning objectives continue to reflect a physician-centered approach focusing on technical competencies that largely lack learning objectives aimed at equipping physicians with the attitudes, knowledge, and skills patients consider essential. 1 When patients’ feedback on the core competencies of a good health care provider is elicited, patients emphasize their providers’ virtuous qualities and compassionate behaviors over qualifications and technical skills, while providers emphasize the opposite. 2 Could the controversy related to integrating the patient’s voice into medical education perhaps be due to this discrepancy between what physicians and medical schools consider to be a core competency and what patients do? If patients’ feedback is valuable to physicians’ ongoing competency development, then medical schools need to adequately train physicians to elicit such feedback. Perhaps the patient’s voice is needed not only in the evaluation of competencies but also in their development. For example, patients could serve as advisors on curriculum committees or as coauthors on letters, such as this one. One practical inhibitor, related to garnering patient feedback on compassion, is the lack of a sufficiently valid and reliable measure to objectively assess patients’ experience of compassion. 3 As a result, compassion is left to the subjective experiences of patients, the good intentions of physicians, and the platitudes of our medical codes of ethics. Recently, we developed a patient-reported compassion measure, the Sinclair Compassion Questionnaire, 4,5 to address the inherent limitations associated with measuring compassion in routine clinical practice. This instrument helps educators, trainees, and physicians assess compassion alongside other quality care indicators and competencies. Mounting evidence suggests that equipping health care providers to provide compassion, while not without challenges, actually has a double effect, benefitting not only patients but increasing physicians’ well-being and job satisfaction also. 2,3
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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 | 0.014 | 0.147 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.019 | 0.038 |
| Insufficient payload (model declined to judge) | 0.017 | 0.012 |
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".