An interdisciplinary knowledge translation intervention in long-term care: Study protocol for the vitamin D and osteoporosis study (ViDOS) pilot cluster randomized controlled trial
Bibliographic record
Abstract
BACKGROUND: Knowledge translation (KT) research in long-term care (LTC) is still in its early stages. This protocol describes the evaluation of a multifaceted, interdisciplinary KT intervention aimed at integrating evidence-based osteoporosis and fracture prevention strategies into LTC care processes. METHODS AND DESIGN: The Vitamin D and Osteoporosis Study (ViDOS) is underway in 40 LTC homes (n = 19 intervention, n = 21 control) across Ontario, Canada. The primary objectives of this study are to assess the feasibility of delivering the KT intervention, and clinically, to increase the percent of LTC residents prescribed ≥800 IU of vitamin D daily. Eligibility criteria are LTC homes that are serviced by our partner pharmacy provider and have more than one prescribing physician. The target audience within each LTC home is the Professional Advisory Committee (PAC), an interdisciplinary team who meets quarterly. The key elements of the intervention are three interactive educational sessions led by an expert opinion leader, action planning using a quality improvement cycle, audit and feedback reports, nominated internal champions, and reminders/point-of-care tools. Control homes do not receive any intervention, however both intervention and control homes received educational materials as part of the Ontario Osteoporosis Strategy. Primary outcomes are feasibility measures (recruitment, retention, attendance at educational sessions, action plan items identified and initiated, internal champions identified, performance reports provided and reviewed), and vitamin D (≥800 IU/daily) prescribing at 6 and 12 months. Secondary outcomes include the proportion of residents prescribed calcium supplements and osteoporosis medications, and falls and fractures. Qualitative methods will examine the experience of the LTC team with the KT intervention. Homes are centrally randomized to intervention and control groups in blocks of variable size using a computer generated allocation sequence. Randomization is stratified by home size and profit/nonprofit status. Prescribing data retrieval and analysis are performed by blinded personnel. DISCUSSION: Our study will contribute to an improved understanding of the feasibility and acceptability of a multifaceted intervention aimed at translating knowledge to LTC practitioners. Lessons learned from this study will be valuable in guiding future research and understanding the complexities of translating knowledge in LTC.
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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 | 0.016 | 0.018 |
| Meta-epidemiology (narrow) | 0.005 | 0.003 |
| Meta-epidemiology (broad) | 0.009 | 0.003 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.055 | 0.007 |
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".