Gender Bias in Collaborative Medical Decision Making: Emergent Evidence
Bibliographic record
Abstract
This initial, exploratory study on gender bias in collaborative medical decision making examined the degree to which physicians' reliance on a team member's patient care advice differs as a function of the gender of the advice giver. In 2018, 283 anesthesiologists read a brief, online clinical vignette and were randomly assigned to receive treatment advice from 1 of 8 possible sources (physician or nurse, man or woman, experienced or inexperienced). They then indicated their treatment decision, as well as the degree to which they relied upon the advice given.The results revealed 2 patterns consistent with gender bias in participants' advice taking. First, when treatment advice was delivered by an inexperienced physician, participants reported replying significantly more on the advice of a man versus a woman, F(1,61) = 4.24, P = .04. Second, participants' reliance on the advice of the woman physician was a function of her experience, F(1,62) = 6.96, P = .01, whereas reliance on the advice of the man physician was not, F(1,60) = 0.21, P = .65.These findings suggest women physicians, relative to men, may encounter additional hurdles to performing their jobs, especially at early stages in their careers. These hurdles are rooted in psychological biases of others, rather than objective features of cases or treatment settings. Cultural stereotypes may shape physicians' information use and decision-making processes (and hinder collaboration), even in contexts that appear to have little to do with social category membership. The authors recommend institutions adopt policies and practices encouraging equal attention to advice, regardless of the source, to help ensure advice taking is a function of information quality rather than the attributes of the advice giver. Such policies and practices may help surface and implement diverse expert perspectives in collaborative medical decision making, promoting better and more effective 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 | 0.139 | 0.428 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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".