Equity in military and Veteran research: Why it is essential to integrate an intersectional sex and gender lens
Bibliographic record
Abstract
LAY SUMMARY For a long time, it has been assumed that to study military members and Veterans means to study men. Further compounding the problem, military and Veteran health research has historically neglected sex and gender issues. This has resulted in systemic biases and gaps in military and Veteran health research that perpetuate existing inequities. However, as this Perspectives piece argues, equity should be a key objective of military and Veteran research. Equity means that the diverse needs of all in the military and Veteran population are considered and addressed. Equity helps ensure fairness and justice in the military and Veteran sector. One of the best ways to advance the goal of equity in research and beyond is to apply an intersectional sex and gender lens. This means, for example, to make visible women’s specific experiences and health outcomes, as well as those of sub-groups of women, men, or gender-diverse military members and Veterans. The author provides tools and considerations for the application of an intersectional sex and gender lens in military, Veteran, and family health research.
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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 | 0.227 | 0.141 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.017 | 0.104 |
| Scholarly communication | 0.027 | 0.039 |
| Open science | 0.004 | 0.017 |
| Research integrity | 0.011 | 0.020 |
| Insufficient payload (model declined to judge) | 0.005 | 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; 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".