Process evaluation of a tailored multifaceted approach to changing family physician practice patterns improving preventive care.
Bibliographic record
Abstract
BACKGROUND: We conducted a process evaluation of a multifaceted outreach facilitation intervention to document the extent to which the intervention was implemented with fidelity. We also hoped to gain insight into how facilitation worked to improve preventive performance. METHODS: We used 5 data collection tools to evaluate the implementation of the intervention, and a combination of descriptive, quantitative, and qualitative analyses. Triangulation was used to attain a complete understanding of the quality of implementation. Twenty-two intervention practices with a total of 54 physicians participated in a randomized controlled trial that took place in Southwestern Ontario, Canada. The key measures of process were the frequency and time involved to deliver intervention components, the scope of the delivery and the utility of the components, and physician satisfaction with the intervention. RESULTS: Of the 7 components in the intervention model, prevention facilitators (PFs) visited the practice most often to deliver the audit and feedback, consensus building, and reminder system components. All the study practices received preventive performance audit and feedback, achieved consensus on a plan for improvement, and implemented a reminder system. Ninety percent of the practices implemented a customized flow sheet, and 10% used a computerized reminder system. Ninety-five percent of the intervention practices wanted critically appraised evidence for prevention, 82% participated in a workshop with opinion leaders in preventive care, and 100% received patient education materials in a binder. Content analysis of the physician interviews and bivariate analysis of physician self-reported changes between intervention and control group physicians revealed that the audit and feedback, consensus building, and development of reminder systems were the key intervention components. Ninety-five percent of the physicians were either satisfied or very satisfied with the intervention, and 90% would have been willing to have the PF continue working with their practice. CONCLUSIONS: Primary care practices in Ontario can implement significant changes in their practice environments that will improve preventive care activity with the assistance of a facilitator. The main components for creating change are audit and feedback of preventive performance, achieving consensus on a plan for improvement, and implementing a reminder system.
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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 | 0.039 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".