INCREASING THE VISIBILITY OF OLDER ADULTS IN RESEARCH: THE IMPORTANCE OF AGE, SEX, AND FRAILTY CONSIDERATIONS
Bibliographic record
Abstract
Abstract Older adults are often considered as a single group with differences between women and men across age groups overlooked. Taking account of sex, age, and their intersection throughout the research process is crucial in minimizing inequities, and to improve the health and wellbeing of the aging population. Age and sex disaggregated data are data separated and analyzed by female, male and age categories, revealing important differences between the sexes and across the life course. This is often missing in research and can result in the oversimplification of study results. As men and women age, they can experience different health conditions, and levels of frailty, leading to the need for tailored treatments. One illustration of this is exploring the burden of disease in Canada, where disaggregating data by sex and age makes key differences in many diseases impacting older adults visible, that may otherwise be missed (for example, heart disease and dementia). Age, sex, and frailty considerations are important throughout the entire continuum of the research process, from question development to dissemination of results to ensure they are accounted for. This presentation will explore why incorporating sex, age, frailty and their intersection are important in health research, how this consideration adds value, and applications of this approach in practice to increase the visibility of older women and men in research, contributing to the ‘fortitude factor’.
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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 | 0.322 | 0.435 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.009 | 0.028 |
| Scholarly communication | 0.024 | 0.025 |
| Open science | 0.003 | 0.025 |
| Research integrity | 0.007 | 0.012 |
| 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".