Voices of senior rural men and women on falls and fall-related injuries: “If I fall outside and get hurt, what would I do?”
Bibliographic record
Abstract
This qualitative study examined the falls and fall-related injury experiences of community-dwelling rural seniors. 42 senior men and women living in two rural areas in Saskatchewan, Canada were recruited, and in-depth interviews were conducted. Analysis revealed three main themes among responses: nature of falls and injuries, causes of falls and injuries, and consequences of falls and injuries. Men and women expressed a fear of falling, which led to activity limitations; however, women were more reflective on their potential to fall and showed an increased level of preparedness compared to men. The causes of falls included activities at the time of a fall, functional limitations, chronic diseases, and personal factors such as type shoes worn. While men and women downplayed the seriousness of their falls or injuries, indicating a level of hardiness, this trend was stronger among men. None of the participants discussed the role of health care professionals or the health care system in relation to fall risk and ways of preventing falls, despite reporting adaptations to prevent and deal with consequences of falls. Overall, these findings may allude to the scarcity of health care services provided in rural communities, highlighting a need to focus on falls prevention for community-dwelling rural seniors.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.008 | 0.006 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".