Engaging primary care practitioners in quality improvement: making explicit the program theory of an interprofessional education intervention
Bibliographic record
Abstract
BACKGROUND: The scientific literature continues to advocate interprofessional collaboration (IPC) as a key component of primary care. It is recommended that primary care groups be created and configured to meet the healthcare needs of the patient population, as defined by patient demographics and other data analyses related to the health of the population being served. It is further recommended that the improvement of primary care services be supported by the delivery of feedback and performance measurements. This paper describes the theory underlying an interprofessional educational intervention developed in Quebec's Montérégie region (Canada) for the purpose of improving chronic disease management in primary care. The objectives of this study were to explain explicitly the theory underlying this intervention, to describe its components in detail and to assess the intervention's feasibility and acceptability. METHOD: A program impact theory-driven evaluation approach was used. Multiple sources of information were examined to make explicit the theory underlying the education intervention: 1) a literature review and a review of documents describing the program's development; 2) regular attendance at the project's committee meetings; 3) direct observation of the workshops; 4) interviews of workshop participants; and 5) focus groups with workshop facilitators. Qualitative data collected were analysed using thematic analysis. RESULTS: The theoretical basis of the interprofessional education intervention was found to be work motivation theory and reflective learning. Five themes describing the workshop objectives emerged from the qualitative analysis of the interviews conducted with the workshop participants. These five themes were the importance of: 1) adopting a regional perspective, 2) reflecting, 3) recognizing gaps between practice and guidelines, 4) collaborating, and 5) identifying possible practice improvements. The team experienced few challenges implementing the intervention. However, the workshop's acceptability was found to be very good. CONCLUSION: Our observation of the workshop sessions and the interviews conducted with the participants confirmed that the objectives of the education intervention indeed targeted the improvement of interprofessional collaboration and quality of care. However, it is clear that a three-hour workshop alone cannot lead to major changes in practice. Long-term interventions are needed to support this complex change process.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".