Conducting a multimodal exercise program in primary and secondary prevention of mobility disabilty in older adults at risk: guidelines and practical applications
Bibliographic record
Abstract
The implementation of strategies to prevent mobility disability in seniors at-risk with a strong focus on exercise is a public health imperative. These strategies must follow a pragmatic, structured and personalized approach. In order to obtain short, medium and long-term benefits, it is essential to consider the coordination of adapted physical exercise programs and to harmonize good practices. In support of national policies for the prevention of loss of autonomy, it is important to define clear guidelines to conduct effective programs. These programs should have a strong emphasis on evidence-based literature and should be validated by a consensus of multi-professional experts. The aim of this consensus is to outline the steps implementing these programs, to present their constituent elements and their practical application. Conception and elaboration of these programs should include frequency, intensity, duration, type of work, volume and individual progressiveness. Programs should also be focused on a personalised approach to develop participant health education, self-efficacy and empowerment for physical activity to ensure long-term health related behaviours. Moreover, trained professionals must supervise these programs in order to assure participants safety and program effectiveness. These guidelines will support policies for the prevention of loss of autonomy and mobility, throughout their development over the national territory.
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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 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".