Gender differences in how physicians access and process information
Bibliographic record
Abstract
There is an absence of information on how physicians make surgical decisions, and on the effect of gender on the processing of information. A novel web based decision-matrix software was designed to trace experimentally the process of decision making in medical situations. The scenarios included a crisis and non-crisis simulation for endometrial cancer surgery. Gynecologic oncologists, fellows, and residents (42 male and 42 female) in Canada participated in this experiment. Overall, male physicians used more heuristics, whereas female physicians were more comprehensive in accessing clinical information (p < 0.03), utilized alternative-based acquisition processes in the non-crisis scenario (p = 0.01), were less likely to consider procedure-related costs (p = 0.04), and overall allocated more time to evaluate the information (p < 0.01). Further experiments leading to a better understanding of the cognitive processes involved in medical decision making could influence education and training and impact on patient outcome.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".