Gender Differences in Medication Adverse Effects Experienced by People Living With Chronic Pain
Bibliographic record
Abstract
Objectives: Understanding gender differences in chronic pain (CP) outcome research is essential to optimal treatment delivery. This study explored the associations between gender identity, gender roles, and the number of non-life-threatening pain medication adverse effects reported as severe by people living with CP. Methods: The analyses were conducted using the COPE Cohort, a dataset generated through a web-based recruitment of adults with CP. Participants were asked how they identified themselves (women, men, unknown, unspecified) and gender roles were measured using the Bem Sex-Role Inventory (subgroups were formed applying the median split method). Pain medication adverse effects were assessed using a standardized checklist (none/mild/moderate/severe). A zero-inflated Poisson model was used to assess gender identity, gender roles and their interaction as potential predictors of the number of pain medication adverse effects. Results: = 0.0030) were associated with the number of pain medication adverse effects reported as severe, and they interacted with each other. The stratified analysis by gender roles showed that women reported a greater number of severe adverse effects than men among those classified as masculine and androgynous. Discussion: Although we are unable to confirm whether the associations can be explained by differences in the experience or in the reporting of effects, gender identity and gender roles should both be explored when studying pain medication adverse effects.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".