Identifying Feasible Physical Activity Programs for Long-Term Care Homes in the Ontario Context
Bibliographic record
Abstract
BACKGROUND: Structured exercise programs for frail institutionalized seniors have shown improvement in physical, functional, and psychological health of this population. However, the 'feasibility' of implementation of such programs in real settings is seldom discussed. The purpose of this systematic review was to gauge feasibility of exercise and falls prevention programs from the perspective of long-term care homes in Ontario, given the recent changes in funding for publically funded physiotherapy services. METHOD: Six electronic databases were searched by two independent researchers for randomized controlled trials that targeted long-term care residents and included exercise as an independent component of the intervention. RESULTS: A total of 39 studies were included in this review. A majority of these interventions were led by physiotherapist(s), carried out three times per week for 30-45 minutes per session. However, a few group-based interventions that were led by long-term care staff, volunteers, or trained non-exercise specialists were identified that also required minimal equipment. CONCLUSION: This systematic review has identified 'feasible' physical activity and falls prevention programs that required minimal investment in staff and equipment, and demonstrated positive outcomes. Implementation of such programs represents cost-effective means of providing long-term care residents with meaningful gains in physical, psychological, and social health.
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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 | 0.017 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".