Improving the diagnosis and treatment of osteoporosis using a senior-friendly peer-led community education and mentoring model: a randomized controlled trial
Bibliographic record
Abstract
Background: This randomized controlled trial (RCT) evaluated a 6-month peer-led community education and mentorship program to improve the diagnosis and management of osteoporosis. Methods: Ten seniors (74–90 years of age) were trained to become peer educators and mentors and deliver the intervention. In the subsequent RCT, 105 seniors (mean age =80.5±6.9; 89% female) were randomly assigned to the peer-led education and mentorship program (n=53) or control group (n=52). Knowledge was assessed at baseline and 6 months. Success was defined as discussing osteoporosis risk with their family physician, obtaining a bone mineral density assessment, and returning to review their risk profile and receive advice and/or treatment. Results: Knowledge of osteoporosis did not change significantly. There was no difference in knowledge change between the two groups (mean difference =1.3, 95% confidence interval [CI] of difference −0.76 to 3.36). More participants in the intervention group achieved a successful outcome (odds ratio 0.16, 95% CI 0.06–0.42, P <0.001). Conclusion: Peer-led education and mentorship can promote positive health behavior in seniors. This model was effective for improving osteoporosis risk assessment, diagnosis, and treatment in a community setting. Keywords: prevention, seniors, mentor, bone mineral density, capacity building, community knowledge translation
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".