Mapping sex and gender differences in falls among older adults: A scoping review
Bibliographic record
Abstract
BACKGROUND: There is growing recognition of the importance of sex and gender differences within falls literature, but the characterization of such literature is uncertain. The aim of this scoping review was to (1) map the nature and extent of falls literature examining sex or gender differences among older adults, and (2) identify gaps and opportunities for further research and practice. METHODS: 60 years and study aims specifying falls and either sex or gender concepts. MEDLINE, Embase, CINAHL, Ageline, and Psychinfo databases were searched from inception to March 2, 2022. Records were screened and charted by six independent reviewers. Descriptive and narrative reports were generated. RESULTS: A total of 15,266 records were screened and 74 studies were included. Most studies reported on sex and gender differences in fall risk factors (n = 52, 70%), incidence/prevalence (n = 26, 35%), fall consequences (n = 22, 30%), and fall characteristics (n = 15, 20%). The majority of studies (n = 70, 95%) found significant sex or gender differences in relation to falls, with 39 (53%) identifying significant sex differences and 31 (42%) identifying significant gender differences. However, only three (4%) studies defined sex or gender concepts and only nine (12%) studies used sex or gender terms appropriately. Fifty-six (76%) studies had more female participants than males. Four (5%) were intervention studies. Studies did not report falls in line with guidelines nor use common fall definitions. CONCLUSION: Sex and gender differences are commonly reported in falls literature. It is critical for future research to use sex and gender terms appropriately and include similar sample sizes across all genders and sexes. In addition, there is a need to examine more gender-diverse populations and to develop interventions to prevent falls that address sex and gender differences among 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 | 0.019 | 0.101 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.019 | 0.017 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".