A Pilot of a Sustainability-Extending Intervention in Canadian Nursing Homes
Bibliographic record
Abstract
Abstract Understanding of intervention sustainability processes is limited. Failure to sustain evidence-based innovations means that intended improvements are short-lived, scale-up and spread are unlikely, and real losses are incurred on research investments. We explored the sustainability of a health care aide (HCA)-led quality improvement (QI) initiative, Safer Care for Older Persons (in residential) Environments (SCOPE), that was implemented in long-term care homes (LTCHs) in Manitoba, Canada. Based on our understanding of factors influencing post-implementation sustainability processes, we developed and piloted a “low-dose” and “high-dose” “Booster” intervention to extend the two-year post-implementation period over which SCOPE was naturally sustained. Both versions of the “Booster” involved the following components: a HCA-led team with management support, a workshop to review SCOPE QI approaches and tools, a binder of QI resources, and supports from an experienced Quality Advisor (QA). We collected data from various sources to depict the most accurate account of QI sustainability and conducted thematic analysis to understand each team’s experience with sustainability processes. We used a qualitative assessment rubric to evaluate the impact of the “Booster” conditions on the teams’ performance against core SCOPE components. Our results suggest that the “Booster” served to establish more relaxed expectations and generally renew interest in LTCH QI initiatives. The calibre of management support was associated with teams’ performance and management support varied with the level of QA support. These pilot results will inform the next study phase, which examines longer-term sustainability of QI initiatives in LTCHs beyond the initial 2-year post-implementation period.
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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 | 0.012 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".