Assessing facilitating conditions and barriers for innovation implementation in Canadian long-term care homes: a research protocol
Bibliographic record
Abstract
BACKGROUND: The COVID-19 pandemic has profoundly affected the health and care of older adults, with particularly negative consequences for those residing in long-term care homes (LTCH) and retirement homes (RH). To inform the implementation of interventions with the most potential for impact, Healthcare Excellence Canada identified six promising practices and policy options that can be introduced to ensure that LTCH and RH are better prepared for potential future outbreaks. A total of 22 implementation science teams (ISTs) were funded to support LTCH and RH across Canada in their implementation of these practices. This study aims to identify the enablers and barriers to the successful implementation of evidence-based practices and the impact of intervention in LTCH and RH across Canada. METHODS: A survey-based longitudinal correlational design will be used. The Organizational Readiness for Knowledge Translation (OR4KT) tool will be used to assess the readiness of LTCH and RH to implement the selected practice. The OR4KT includes 59 questions and takes about 15 min to complete. Five to ten respondents per organization, holding different job positions, will be invited by the ISTs to complete the OR4KT in 91 LTCH or RH across Canada at the beginning of the project (T1) and 6 months after the first measurement (T2). DISCUSSION: The study will provide a benchmark for assessing the readiness of LTCH and RH to implement evidence-based practices. It will also inform decision-makers about barriers and facilitators that influence the integration of promising practices in these organizations.
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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 | 0.041 | 0.029 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.008 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.016 | 0.004 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.007 | 0.005 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.029 | 0.003 |
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".