Factors Affecting the Sustainment, Sustainability, and Spread of Practice Changes in Canadian Long-Term Care Homes
Bibliographic record
Abstract
Abstract Our understanding of the post-implementation sustainment, sustainability, and spread (SSS) of complex quality improvement interventions is limited. We explored factors that influenced the SSS of a care aide-led quality improvement initiative (Safer Care for Older Persons (in residential) Environments [SCOPE]) implemented in 6 Manitoba long-term care homes two years after the conclusion of SCOPE in 2017. We analyzed small group interview data collected from all unit- and facility-level managers who participated in SCOPE and were still working in these facilities. We asked about SCOPE implementation, post-SCOPE quality improvement activities, factors that influenced them, and about inter-unit spread of SCOPE following the project’s conclusion. The interviews were audio-recorded, transcribed verbatim, de-identified, and analyzed using thematic analysis. Five of the 6 facilities reported sustained SCOPE quality improvement activities, tools, and facilitative structures. In the same 5 facilities, SCOPE benefits (e.g., increases in care aide empowerment and self-efficacy, manager belief in care aide capacity) continued post-implementation. Spread beyond the original SCOPE units had occurred in 3 facilities. Factors that influenced the SSS of SCOPE were related to the team (e.g., care aides' quality improvement capacity), to the unit and facility (e.g., culture of innovation and change), and to the long-term care system (e.g., competing imperatives). Some factors influencing SSS differ from factors known to influence implementation. The identified factors affecting SSS highlight the influence of social dynamics (i.e., interactions, communication, relationships) among staff on SSS. Further research is warranted to explore interactions among these influencing factors and how they lead to SSS.
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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 | 0.008 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.009 | 0.003 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".