Reporting and analysis of Sex and Gender in Transitions of Care for Older Adults: A Methods Study
Bibliographic record
Abstract
Objectives: Sex and gender based analysis may improve understanding of transitions from long term care to community or health services. Our study aims to assess how sex or gender was reported and analyzed in studies about care transitions for older adults. Methods: We identified longitudinal studies from a 2017 scoping review on factors affecting care transitions of older adults (participants 60 years and older) and assessed transitions from long-term care to community or health services. We used a pretested coding sheet to assess the reporting of sex or gender in 5 sections of the studies: title and abstract, introduction, methods, results, and discussion. Results: We included 20 longitudinal studies conducted in 3 countries (United States, Germany, and Finland ) with the study durations ranging from less than 1 year to 10 years. Almost all of the studies reported sex distribution of the sample (18/20; 90%). Sex or gender was discussed in the background and rationale by three out of 20 studies (15%). Twelve studies (60%; 12/20) planned to control for sex or gender in their statistical analysis. Association of sex with outcomes was reported by 45% (9/20) and 3 studies (15%; 3/20) provided disaggregated data on sex or gender. Conclusion: Almost half of the studies assessing transitions from long-term care to community or health services did not control for sex or gender in their statistical analysis. This may be a missed opportunity for understanding potential sex or gender differences in transitions in care for older adults.
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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 | 0.241 | 0.311 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.009 |
| Bibliometrics | 0.011 | 0.012 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".