Bibliographic record
Abstract
Older adults now represent more than a billion people worldwide, and the majority are women.1 The number of older adults in Canada aged 65 years and older exceeds younger people, under 14 years of age, yet the health and social needs of older adults, particularly older women, remain largely underrecognized and unmet.2,3 The onesize-fits-all approach to the study of aging, where age categories, sex, and gender-related sociocultural factors including socioeconomic status are not incorporated, is inadequate.As a result, older women and their unique health and social needs remain largely invisible.4 Women's Age Lab at Women's College Hospital, a research center focused on improving the health and wellbeing of older women, released a timely report "Women at the Forefront of Aging in Canada" to address this research gap. 5 This report brings attention to the health and well-being challenges women encounter as they age, while also emphasizing their unique needs to ensure they are more effectively recognized and addressed.Understanding the needs of older women and men through research equips geriatricians in providing tailored care for older adults, but this has not always been the case.For example, women experience more adverse drug events than men 6 and adverse events increase with age.Yet, it was not until the 1990s that the National Institutes of Health mandated the inclusion of women in studies they funded 7 and not until more recently the inclusion of older adults.8 This early lack of inclusion of older women in clinical trials resulted in some drug
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.039 | 0.008 |
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".