FALLS IN MID-LIFE: A SCOPING LITERATURE REVIEW
Bibliographic record
Abstract
Abstract The implications of falls of middle-aged adults (40-64 years) on falling in late life (65+) have not received much attention. The patterns of falling over the lifespan require more research. The purpose of this scoping literature review was to answer: What is known about falls occurring in mid-life? and How falls in mid-life relate to falling over the adult lifespan? A six-stage scoping literature review framework by Levac et al. (2010) was followed. A total of 5,136 titles were identified in CINAHL, EMBASE and MEDLINE using MeSH terms for accidental fall, middle-aged and longitudinal studies. Inclusion/exclusion criteria narrowed the search to 30 full-text research articles and nine gray literature sources that were charted. Literature on falls in mid-life produced five discrete themes: two distinct populations of fallers, prevalence rates, fall-related injuries, causes of falls, and risk factors for falling. The two groups of fallers were the general population (falls prevalence 8.7-35.8%) and the special population of mid-life adults living with chronic health conditions (falls prevalence 26% for diabetes and 32.3% intellectual disabilities). Middle-aged adults had a higher proportion of injurious falls (11.5-30%) compared to older adults and extrinsic risk factors were the most frequent causes of falling (83.3%). For special populations, the risk of falls was frequently attributed to intrinsic factors. In conclusion, falls in mid-life require further exploration to establish patterns over the adult lifespan, determine influence of chronic diseases, establish clear fall incidences and risk factors, and determine if current falls prevention interventions are appropriate for mid-life 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 | 0.009 | 0.044 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.030 | 0.026 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.005 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".