Insights on a National Safety Improvement Learning Collaborative: Using the Consolidated Framework for Implementation Research
Bibliographic record
Abstract
Background: There is a growing interest in quality improvement collaboratives (QICs), even though less remains known about contextual factors that impact collective and local project implementation. A study was undertaken that used the Consolidated Framework for Implementation Research (CFIR) to explore the contextual factors impacting the use of this nationwide QIC in Canada. Methods: A deductive or direct qualitative content analysis using CFIR was employed to explore the contextual factors impacting the implementation of a nationwide QIC and participating organizations. Data were used from document analysis and semi-structured interviews with participants from 30 participating healthcare organizations across Canada. Results: A variety of contextual factors emerged, which influenced the uptake of the QICs across different settings, including intervention characteristics, outer setting, inner setting, and process factors. This study illustrates how organizations can consider a multi-pronged, theory-driven approach to guide the evaluation of safety and quality improvement efforts. Conclusions: This study provides insights into contextual factors that impact the implementation of local safety projects involved in a larger QIC, which may serve as a template or blueprint for healthcare leaders in their efforts to guide the co-design, implementation and evaluation of safety and quality improvement efforts.
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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 | 0.244 | 0.149 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.012 | 0.012 |
| Science and technology studies | 0.013 | 0.038 |
| Scholarly communication | 0.025 | 0.019 |
| Open science | 0.007 | 0.018 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".