Strategies to overcome barriers to implementing osteoporosis and fracture prevention guidelines in long-term care: a qualitative analysis of action plans suggested by front line staff in Ontario, Canada
Bibliographic record
Abstract
BACKGROUND: Osteoporosis is a major global health problem, especially among long-term care (LTC) facilities. Despite the availability of effective clinical guidelines to prevent osteoporosis and bone fractures, few LTC homes actually adhere to these practical recommendations. The purpose of this study was to identify barriers to the implementation of evidence-based practices for osteoporosis and fracture prevention in LTC facilities and elicit practical strategies to address these barriers. METHODS: We performed a qualitative analysis of action plans formulated by Professional Advisory Committee (PAC) teams at 12 LTC homes in the intervention arm of the Vitamin D and Osteoporosis Study (ViDOS) in Ontario, Canada. PAC teams were comprised of medical directors, administrators, directors of care, pharmacists, dietitians, and other staff. Thematic content analysis was performed to identify the key themes emerging from the action plans. RESULTS: LTC teams identified several barriers, including lack of educational information and resources prior to the ViDOS intervention, difficulty obtaining required patient information for fracture risk assessment, and inconsistent prescribing of vitamin D and calcium at the time of admission. The most frequently suggested recommendations was to establish and adhere to standard admission orders regarding vitamin D, calcium, and osteoporosis therapies, improve the use of electronic medical records for osteoporosis and fracture risk assessment, and require bone health as a topic at quarterly reviews and multidisciplinary conferences. CONCLUSIONS: This qualitative study identified several important barriers and practical recommendations for improving the implementation of osteoporosis and fracture prevention guidelines in LTC settings.
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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 | 0.018 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.020 | 0.012 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".