Teaching negotiation skills to medical trainees enhances their leadership development
Bibliographic record
Abstract
In health care, negotiation is a crucial skill that physicians apply in many contexts, from delegating clinical duties to navigating work terms. Various strategies and approaches can improve the efficacy of these interactions, and it is increasingly important for medical curricula to be adapted in a way that fosters the development of certain skill sets centred around leadership. Negotiation falls into this category and is crucial in developing both management and clinical capacities. Although the literature identifies the relation between knowledge and skill in negotiating, there has been limited integration into curricular activities. This article provides an overview of negotiation strategies as examined in the literature. It includes the commonly used positional negotiation strategy as well as the more effective principled negotiation strategy developed by the Harvard Negotiation Project. We compare the usefulness of these two strategies using a real-world scenario and summarize the literature exploring the gap in the skill of negotiation among trainees. This can also serve to identify ways in which it can be incorporated as a standard in medical education. Overall, with the push for leadership development, we propose that negotiation should not be a skill that is expected to be gained through work experience, but as a formal part of the medical education curriculum.
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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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".