Design characteristics and inclusion of evidence-based exercise recommendation in fall prevention community exercise programs for older adults in Canada: a national descriptive self-report study
Bibliographic record
Abstract
BACKGROUND: Training balance through exercise is an effective strategy to reduce falls in community-dwelling older adults. Evidence-based fall prevention exercise recommendations have been proposed, specifying that exercise programs should: (1) provide a high challenge to balance, (2) be offered for a least three hours per week, (3) be provided on an ongoing basis. Community exercise programs have the potential to deliver effective fall prevention exercise; however, current design characteristics and whether they include the recommendations is not known. This study described design characteristics of fall prevention community exercise programs for older adults (50 years and older) across Canada, and explored whether these programs included the three evidence-based exercise recommendations. METHODS: Instructors of fall prevention community exercise programs completed electronic self-report questionnaires following a modified Dillman recruitment approach. Questions explored program characteristics, exercise content, target population, and program and instructor demographic information. Using a previously developed coding scheme based on recommendations, exercises were coded for balance challenge. RESULTS: One hundred fourty completed eligible questionnaires were analyzed (74% response rate). One hundred thirty-three programs (95%) included the challenge recommendation by prescribing mostly moderate or high challenge balance exercises, 16 programs (11%) included at least three hours of exercise a week, and 59 programs (42%) were offered on an ongoing basis. Eight programs (6%) included all three recommendations. CONCLUSIONS: Most programs included at least one recommendation for effective fall prevention exercise. Future studies should examine organizational barriers and facilitators to incorporating evidence-based exercise recommendations and explore the use of mixed home/in-class strategies to include the recommendations.
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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 | 0.008 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".