Fracture prevention medications.
Bibliographic record
Abstract
<div> Falls and hip fractures are a major health concern among older adults in long term care (LTC) with almost 50% of residents experiencing a fall annually. Hip fractures are one of the most important and frequent fall-related injuries in LTC. There is moderate to strong certainty evidence that multifactorial interventions may reduce the risk of falls and fractures; however, there is little evidence to support its implementation. The purpose of this study was to determine the feasibility (recruitment rate and adaptations) with a subobjective to understand facilitators to and barriers of implementing the PREVENT (Person-centred <u>R</u>outine Fracture Pre<u>EVENT</u>ion) model in practice. The model includes a multifactorial intervention on diet, exercise, environmental adaptations, hip protectors, medications (including calcium and vitamin D), and medication reviews to treat residents at high risk of fracture. Our secondary outcomes were to determine if there was a change in knowledge uptake of the guidelines among healthcare providers and in the proportion of fracture prevention prescriptions post-intervention. We conducted a mixed-methods longitudinal cohort study in three LTC homes across southern Ontario. A local champion was selected to help guide the implementation of the model and promote best practices. We reported recruitment rates using descriptive statistics and challenges to implementation using content analysis. We reported changes in knowledge uptake and in the proportion of fracture prevention medications using the McNemar’s test. We recruited three LTC homes and identified one local champion for each home. We required two months to identify and train the local champion over three, 1.5-hour train-the-trainer sessions, and the local champion required three months to deliver the intervention to a team of healthcare professionals. We identified several facilitators, barriers, and adaptations to PREVENT. Benefits of the model include easy access to the Fracture Risk Scale (FRS), clear and succinct educational material catered to each healthcare professional, and an accredited Continuing Medical Educational module for physicians and nurses. Challenges included misperceptions between the differences in fall and fracture prevention strategies, fear of perceived side effects associated with fracture prevention medications, and time barriers with completing the audit report. Our study found an increase knowledge uptake of the guidelines and an increase in the proportion of fracture prevention prescriptions post-intervention. </div>
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.540 | 0.052 |
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; both teacher heads agree on what is shown here.
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".