Closing the empathy gap towards equitable outcomes: gender equity in the medical workforce
Bibliographic record
Abstract
BACKGROUND: Empathy failures lead to equity failures. Women and men physicians experience work differently. Men physicians, however, may be unaware how these differences impact their colleagues. This constitutes an empathy gap; empathy gaps are associated with harm to outgroups. In our previous published work, we found that men had divergent views from women about the experiences of women relating to gender equity; senior men differed most from junior women. Since men physicians hold disproportionately more leadership roles than women, this empathy gap warrants exploration and remediation. ANALYSIS: Gender, age, motivation and power each seems to influence our empathic tendencies. Empathy, however, is not a static trait. Empathy can be developed and displayed by individuals through their thoughts, words and actions. Leaders can also influence culture by enshrining an empathic disposition in our social and organisation structures. CONCLUSIONS: We outline methods to increase our empathic capacities as individuals and organisations through perspective-taking, perspective-giving and verbal commitments to institutional empathy. In doing so, we challenge all medical leaders to herald an empathic transformation of our medical culture in pursuit of a more equitable and pluralistic workplace for all groups of people.
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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 | 0.019 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.000 |
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".