Successful knowledge translation intervention in long-term care: final results from the vitamin D and osteoporosis study (ViDOS) pilot cluster randomized controlled trial
Bibliographic record
Abstract
BACKGROUND: Few studies have systematically examined whether knowledge translation (KT) strategies can be successfully implemented within the long-term care (LTC) setting. In this study, we examined the effectiveness of a multifaceted, interdisciplinary KT intervention for improving the prescribing of vitamin D, calcium and osteoporosis medications over 12-months. METHODS: We conducted a pilot, cluster randomized controlled trial in 40 LTC homes (21 control; 19 intervention) in Ontario, Canada. LTC homes were eligible if they had more than one prescribing physician and received services from a large pharmacy provider. Participants were interdisciplinary care teams (physicians, nurses, consultant pharmacists, and other staff) who met quarterly. Intervention homes participated in three educational meetings over 12 months, including a standardized presentation led by expert opinion leaders, action planning for quality improvement, and audit and feedback review. Control homes did not receive any additional intervention. Resident-level prescribing and clinical outcomes were collected from the pharmacy database; data collectors and analysts were blinded. In addition to feasibility measures, study outcomes were the proportion of residents taking vitamin D (≥800 IU/daily; primary), calcium ≥500 mg/day and osteoporosis medications (high-risk residents) over 12 months. Data were analyzed using the generalized estimating equations technique accounting for clustering within the LTC homes. RESULTS: At baseline, 5,478 residents, mean age 84.4 (standard deviation (SD) 10.9), 71% female, resided in 40 LTC homes, mean size = 137 beds (SD 76.7). In the intention-to-treat analysis (21 control; 19 intervention clusters), the intervention resulted in a significantly greater increase in prescribing from baseline to 12 months between intervention versus control arms for vitamin D (odds ratio (OR) 1.82, 95% confidence interval (CI): 1.12, 2.96) and calcium (OR 1.33, 95% CI: 1.01, 1.74), but not for osteoporosis medications (OR 1.17, 95% CI: 0.91, 1.51). In secondary analyses, excluding seven nonparticipating intervention homes, ORs were 3.06 (95% CI: 2.18, 4.29), 1.57 (95% CI: 1.12, 2.21), 1.20 (95% CI: 0.90, 1.60) for vitamin D, calcium and osteoporosis medications, respectively. CONCLUSIONS: Our KT intervention significantly improved the prescribing of vitamin D and calcium and is a model that could potentially be applied to other areas requiring quality improvement. TRIAL REGISTRATION: ClinicalTrials.gov: NCT01398527 . Registered: 19 July 2011.
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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 | 0.026 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 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) | 0.000 | 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 teacher head, 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".