Bibliographic record
Abstract
BACKGROUND: When approaching a new research area such as risk and resilience in diverse underrepresented communities, it can be enlightening to turn to an area of diversity in which much research has already been conducted: sex differences, gender, and women's health. Like other diversities, women and females have been sorely underrepresented in clinical studies and non-human animal research. Different strategies for inclusion have emerged. OBJECTIVES: To (i) identify what is known about sex and gender in resilience; (ii) consider key unknowns including the intersections of Sex and Gender with SSDH; (iii) provide recommendations for future inclusion of sex and gender as an intersecting factor in diverse populations' resilience in aging and dementia. METHOD: Consolidating the talks of five experts in sex and gender and aging research and determining the themes emerging, we synthesize key recommendations for integrating sex and gender and SSDH. RESULTS: Human diversity intersects with the multiple biologies of sex and the social worlds of gender. Including SSDH increases the complexity of analysis but leads to more precise understandings of aging. Lessons learned from women's health research teach us that there is a myriad of biologies within a single classification (eg., number of children, type of menopause) leading to multiple aging trajectories requiring that we not restrict ourselves to large cohort studies but instead, dive deeply into single groups. CONCLUSION: Sex and gender research provides helpful recommendations for both studying diverse populations as well as integrating sex and gender in diversity in resilience 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 | 0.113 | 0.076 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.010 | 0.057 |
| Scholarly communication | 0.011 | 0.016 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.006 | 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; 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".