Implementation of the Single Site Order in Long-Term Care: What We Can Learn from Using the Consolidated Framework for Implementation Research
Bibliographic record
Abstract
Context: To mitigate the risk of spread of COVID-19 in long-term care (LTC), the Public Health Agency of Canada instituted several rapid redesign and resource redeployment practices, including single-site policies. Objective: This study aims to understand factors that influence implementation of the Single Site Order (SSO). Methods: Consolidated Framework for Implementation Research (CFIR) guided data collection and analysis. Ten leadership team members and 18 staff were interviewed across 4 LTC homes in British Columbia (BC), Canada. In NVivo 12, a deductive framework analysis was used. Findings: Seven notable CFIR constructs (intervention source, evidence strength and quality, costs, culture, networks and communication, readiness for implementation, and patient needs and resources) were found to be most influential in the implementation of the SSO. We present these constructs and the factors within. Limitations: Our study was limited to the BC context. However, we believe that the findings offer useful insights into the complexity of policy implementation in LTC. Implications: In a system already facing staffing concerns and a highly dependent and increasingly frail resident population, implementation of the SSO further taxed already stretched resources.
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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 | 0.454 | 0.406 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.008 | 0.037 |
| Scholarly communication | 0.026 | 0.028 |
| Open science | 0.009 | 0.013 |
| Research integrity | 0.007 | 0.013 |
| 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".