How to Convince Clinicians that ‘Soft’ Skills Save Lives? Practical Tips to Use Clinical Studies to Teach Physicians’ Roles
Bibliographic record
Abstract
This article was migrated. The article was marked as recommended. The implementation of competency-based medical education is hampered by unsupported arguments like 'soft' skills are important, but they don't save lives. When implementing teaching and assessment methods targeting non-medical expert roles, student and physician buy-in is crucial. These intrinsic roles (e.g. collaborator or professional) are unfortunately misinterpreted and underused by supervisors, in part because of the false assumption that those skills have minimal impact on patient outcomes. On the contrary, although not worded in those terms, many clinical studies prove the impact of those roles on patient mortality, morbidity, readmission rate, or compliance. Whereas physicians feel that they are properly trained to give feedback, they struggle in making this connection between clinical studies and intrinsic roles in their everyday teaching habits. In this article, we provide practical tips on why and how to use high-impact clinical studies to enlighten supervisors and trainees about the educational and clinical importance of those skills. A slide kit, to be presented in clinical settings, provides a selection of 30 examples of 'hard' evidence on those so-called 'soft' skills, reinforcing the fact that intrinsic roles are intertwined with the medical expert role to improve patient care.
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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 | 0.044 | 0.150 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.008 | 0.015 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.012 | 0.014 |
| Insufficient payload (model declined to judge) | 0.025 | 0.022 |
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".