Limitations of the Male/Female Binary for Studying the Influences of Sex‐ and Gender‐Related Factors on Health
Bibliographic record
Abstract
Momentum has been building for several decades around the value of incorporating sex and gender considerations in biomedical, clinical, and health research more broadly. In that period, there has been a proliferation of guidelines, policies, definitions, methods, and conceptual frameworks for doing so, which is both constructive and challenging: the diversity of concepts and methods generates knowledge that highlights different aspects of the phenomena under study, but at the same time, it can create inconsistency and fragmentation around the operationalization and interpretation of research attending to sex and gender considerations in health. A male-female binary approach to examining how sex and gender influence health has predominated in many domains, and although this has value for helping to identify health disparities related to sex and gender, there are also some important limitations of an uncritical overreliance on male-female comparisons; three case studies from the biomedical literature are used to help illustrate some of these limitations. Ultimately, there is no single correct approach to addressing sex and gender in health research. I contend that the most crucial element is that researchers need to bring careful and critical attention to the incorporation of sex and gender considerations in ways that are appropriate for their research context and understand and articulate the limitations of their chosen approaches.
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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 | 0.343 | 0.472 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.010 | 0.025 |
| Scholarly communication | 0.008 | 0.013 |
| Open science | 0.009 | 0.013 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".