Patient engagement in an academic community-based primary care practice’s management committee: A case study
Bibliographic record
Abstract
Patient engagement in primary care has been the focus of many studies; however, little research has evaluated its added value to organisational management in an academic community-based primary care practice (ACBPCP). In 2017, managers of an ACBPCP in Montreal, Canada, decided to integrate patients into the organization’s management committee to enhance the quality and relevance of decision-making for clinical services, education and research. Objectives were to 1) assess patients’ role and influence on an ACBPCP management committee’s decision-making process; 2) identify the facilitators of and obstacles to patient involvement in this context; and 3) evaluate the impact of this innovative approach in promoting a patient partnership culture throughout the organization. Using a single case study, qualitative and quantitative data was collected between June 2017 and May 2019 from three levels: 1) professionals in charge of patient partnership working within the territorial health care organization’s quality division; 2) management committee; and 3) ACBPCP’s staff outside the committee. Successful patient governance relies on a structured engagement approach, including a rigorous recruitment process, joined training and coaching of all committee members and the development of work modalities that facilitate co-construction. Multilevel leadership is also fundamental to support a partnership culture throughout the organisation. The results of this study illustrate opportunities and challenges related to patient involvement at an ACBPCP’s organizational level. They can guide other community-based primary care practices interested in involving patients in their management activities. Experience Framework This article is associated with the Patient, Family & Community Engagement lens of The Beryl Institute Experience Framework (https://www.theberylinstitute.org/ExperienceFramework). Access other PXJ articles related to this lens. Access other resources related to this lens.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.004 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.005 |
| Insufficient payload (model declined to judge) | 0.000 | 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".