Sex bias in pain management decisions
Bibliographic record
Abstract
In the pursuit of mental and physical health, effective pain management stands as a cornerstone. Here, we examine a potential sex bias in pain management. Leveraging insights from psychological research showing that females' pain is stereotypically judged as less intense than males' pain, we hypothesize that there may be tangible differences in pain management decisions based on patients' sex. Our investigation spans emergency department (ED) datasets from two countries, including discharge notes of patients arriving with pain complaints (N = 21,851). Across these datasets, a consistent sex disparity emerges. Female patients are less likely to be prescribed pain-relief medications compared to males, and this disparity persists even after adjusting for patients' reported pain scores and numerous patient, physician, and ED variables. This disparity extends across medical practitioners, with both male and female physicians prescribing less pain-relief medications to females than to males. Additional analyses reveal that female patients' pain scores are 10% less likely to be recorded by nurses, and female patients spend an additional 30 min in the ED compared to male patients. A controlled experiment employing clinical vignettes reinforces our hypothesis, showing that nurses (N = 109) judge pain of female patients to be less intense than that of males. We argue that the findings reflect an undertreatment of female patients' pain. We discuss the troubling societal and medical implications of females' pain being overlooked and call for policy interventions to ensure equal pain treatment.
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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 | 0.015 | 0.068 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".