Building a Program Theory of Implementation Using Process Evaluation of a Complex Quality Improvement Trial in Nursing Homes
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Significant quality problems exist in long-term care (LTC). Interventions to improve care are complex and often have limited success. Implementation remains a black box. We developed a program theory explaining how implementation of a complex intervention occurs in LTC settings-examining mechanisms of impact, effects of context on implementation, and implementation outcomes such as fidelity. RESEARCH DESIGN AND METHODS: Concurrent process evaluation of Safer Care for Older Persons in residential Environments (SCOPE)-a frontline worker (care aide) led improvement trial in 31 Canadian LTC homes. Using a mixed-methods exploratory sequential design, qualitative data were analyzed using grounded theory to develop a conceptual model illustrating how teams implemented the intervention and how it produced change. Quantitative analyses (mixed-effects regression) tested aspects of the program theory. RESULTS: Implementation fidelity was moderate. Implementation is facilitated by (a) care aide engagement with core intervention components; (b) supportive leadership (internal facilitation) to create positive team dynamics and help negotiate competing workplace priorities; (c) shifts in care aide role perceptions and power differentials. Mixed-effects model results suggest intervention acceptability, perceived intervention benefits, and leadership support predict implementation fidelity. When leadership support is high, fidelity is high regardless of intervention acceptability or perceived benefits. DISCUSSION AND IMPLICATIONS: Our program theory addresses important knowledge gaps regarding implementation of complex interventions in nursing homes. Results can guide scaling of complex interventions and future research.
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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 | 0.144 | 0.234 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".