Sex, Gender, and the Regulation of Prescription Drugs: Omissions and Opportunities
Bibliographic record
Abstract
The regulation of prescription drugs is an important health, safety, and equity issue. However, regulatory processes do not always consider evidence on sex, gender, and factors such as age and race, omissions that advocates have highlighted for several decades. Assessing the impact of sex-related factors is critical to ensuring drug safety and efficacy for females and males, and for informing clinical product monographs and consumer information. Gender-related factors affect prescribing, access to drugs, needs and desires for specific prescribed therapies. This article draws on a policy-research partnership project that examined the lifecycle management of prescription drugs in Canada using a sex and gender-based analysis plus (SGBA+) lens. In the same time period, Health Canada created a Scientific Advisory Committee on Health Products for Women, in part to examine drug regulation. We report on grey literature and selected regulatory documents to illustrate the extent to which sex and gender-based analysis plus (SGBA+) is utilized in regulation and policy. We identify omissions in the management of prescription drugs, and name opportunities for improvements by integrating SGBA+ into drug sponsor applications, clinical trials development, and pharmacovigilance. We report on recent efforts to incorporate sex disaggregated data and recommend ways that the management of prescription drugs can benefit from more integration of sex, gender, and equity.
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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 | 0.014 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".