Challenges with embedding an integrated sex and gender perspective into pain research: Recommendations and opportunities
Bibliographic record
Abstract
The focus of this article, within this BBI horizons special issue, is on sex, gender, and pain. We summarise what is currently known about sex- and gender-related variations in pain, exploring intersectional biological and psychosocial mechanisms, and highlight gaps in knowledge and understanding. Five key challenges with the exploration of sex and gender in pain research are presented, relating to: conceptual imprecision, research bias, limitations with binary descriptions, integrating sex and gender, and timely adoption/implementation of good research practice. Guidance on how to overcome such challenges is provided. Despite clear evidence for sex and gender differences in pain, there are conceptual and methodological barriers to overcome. Innovation in methods and approach can help develop more effective and tailored treatment approaches for men, women, boys, girls, and gender-diverse 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 | 0.340 | 0.375 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.005 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.008 | 0.028 |
| Scholarly communication | 0.023 | 0.058 |
| Open science | 0.009 | 0.029 |
| Research integrity | 0.019 | 0.037 |
| Insufficient payload (model declined to judge) | 0.014 | 0.004 |
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; the direct Gemma label and the distilled Codex classifier 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".