Influences of post-implementation factors on the sustainability, sustainment, and intra-organizational spread of complex interventions
Bibliographic record
Abstract
BACKGROUND: Complex interventions are increasingly applied to healthcare problems. Understanding of post-implementation sustainment, sustainability, and spread of interventions is limited. We examine these phenomena for a complex quality improvement initiative led by care aides in 7 care homes (long-term care homes) in Manitoba, Canada. We report on factors influencing these phenomena two years after implementation. METHODS: Data were collected in 2019 via small group interviews with unit- and care home-level managers (n = 11) from 6 of the 7 homes using the intervention. Interview participants discussed post-implementation factors that influenced continuing or abandoning core intervention elements (processes, behaviors) and key intervention benefits (outcomes, impact). Interviews were audio-recorded, transcribed verbatim, and analyzed with thematic analysis. RESULTS: Sustainment of core elements and sustainability of key benefits were observed in 5 of the 6 participating care homes. Intra-unit intervention spread occurred in 3 of 6 homes. Factors influencing sustainment, sustainability, and spread related to intervention teams, unit and care home, and the long-term care system. CONCLUSIONS: Our findings contribute understanding on the importance of micro-, meso-, and macro-level factors to sustainability of key benefits and sustainment of some core processes. Inter-unit spread relates exclusively to meso-level factors of observability and practice change institutionalization. Interventions should be developed with post-implementation sustainability in mind and measures taken to protect against influences such as workforce instability and competing internal and external demands. Design should anticipate need to adapt interventions to strengthen post-implementation traction.
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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 | 0.030 | 0.106 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".