The role of the practice facilitators in Ontario primary healthcare quality improvement
Bibliographic record
Abstract
BACKGROUND: Practice facilitation is a key component of quality improvement in primary healthcare. Studies have reported the effectiveness of practice facilitation in improving quality management and care delivery. However, little has been published about practice facilitators' training, facilitation activities, and their perceived role in quality improvement in primary healthcare. This study examined practice facilitators' training and the perceptions of the practice facilitator role in a provincial primary healthcare learning collaborative quality improvement initiative in Ontario, Canada. METHOD: Descriptive and qualitative methods were used to outline the practice facilitator training as well as to look into the experiences and perceptions of practice facilitators and primary healthcare teams regarding the practice facilitation role in quality improvement. Data collection included training artifacts, activity logs, self-reflection reports, and semi-structured interviews with practice facilitators and primary healthcare participants. Reflections and interviews were analyzed to identify the role of the practice facilitators from their own experience, and from the perspective of the participants. Descriptive statistics were used to learn about categories of facilitation activities undertaken and frequency of these activities. RESULTS: Sixteen practice facilitators and seven family healthcare teams participated in the study. Practice facilitators received a two-day intensive training workshop and continued training. Their time was spent mostly working directly with participating teams, continued learning and training, communications and administration. They served as coaches, resource providers, enablers and motivators. Participating teams expressed satisfaction with the practice facilitator role, although they had hoped this position would provide onsite and hands-on support in conducting activities of quality improvement at the practice level. CONCLUSIONS: Practice facilitators played a crucial role in the implementation of quality improvement in Ontario's learning collaborative program. The practice facilitator role is perceived to be that of a coach, enabler and motivator. This study suggests that the practice facilitator successfully supported participating teams to undertake quality improvement activities in primary healthcare settings.
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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 | 0.012 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.004 |
| 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".