Primary care quality improvement from a practice facilitator’s perspective
Bibliographic record
Abstract
BACKGROUND: Practice facilitation has proven to be effective at improving care delivery. Practice facilitators are healthcare professionals who work with and support other healthcare providers. To the best of our knowledge, very few studies have explored the perspective of facilitators. The objective of this study was to gain insight into the barriers that facilitators face during the facilitation process and to identify approaches used to overcome these barriers to help practices move towards positive change. METHODS: We conducted semi-structured interviews with four practice facilitators who worked with 84 primary care practices in Eastern Ontario, Canada over a period of five years (2007-2012). The transcripts were analyzed independently by three members of the research team using an open coding technique. A qualitative data analysis using immersion/crystallization technique was applied to interpret the interview transcripts. RESULTS: Common barriers identified by the facilitators included accessibility to the practice (e.g., difficulty scheduling meetings, short meetings), organizational behaviour (team organization, team conflicts, etc.), challenges with practice engagement (e.g., lack of interest, lack of trust), resistance to change, and competing priorities. To help practices move towards positive change the facilitators had to tailor their approach, integrate themselves, be persistent with practices, and exhibit flexibility. CONCLUSIONS: The consensus on redesigning and transforming primary care in North America and around the world is rapidly growing. Practice facilitation has been pivotal in materializing the transformation in the way primary care practices deliver care. This study provides an exclusive insight into facilitator approaches which will assist the design and implementation of small- and large-scale facilitation interventions.
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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 | 0.031 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| 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".