An Exercise And Education Program For Adult’s Post DRF: An Intervention Map
Bibliographic record
Abstract
PURPOSE: to map the intervention onto major domains to gain a better understanding of the targeted outcomes METHODS: The Hands Up program was designed to provide exercise and education for people at risk of developing fragility fractures. A systematic approach was taken to identify the key competencies of the program. The competencies were then mapped to the following categories: skill building; attitudes, beliefs, cognitions; learning strategies; platform; use, adherence, and fidelity tracking; mechanism outcome indicators; person health impacts expected and system impacts RESULTS: The key competencies identified were understand the risk of osteoporosis, communicating with doctors, self-manage non-pharmacological osteoporosis treatment strategies, fracture prevention, home fall hazard identification, identify nutrition recommendations for osteoporosis and perform osteoporosis targeted exercises. The intervention map highlights that the intervention provides the tools for the patients to increase their knowledge of their condition and how to manage the condition independently. This also improves patient’s attitude and beliefs, prepare them for a healthy future and reduce their risk of fractures. CONCLUSIONS: This program can lead to system wide positive impacts. It has potential to decrease the costs related to fractures/injuries and can improve shared decision making between clinicians and patients within the healthcare system.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 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".