Coaching strategies for enhancing practice transformation
Bibliographic record
Abstract
BACKGROUND: Current research on primary care practice redesign suggests that outside facilitation can be an important source of support for achieving substantial change. OBJECTIVES: To analyse the specific sequence of strategies used by a successful practice facilitator during the American Academy of Family Physicians' (AAFP) National Demonstration Project (NDP). METHODS: This secondary analysis describes a sequence of strategies used to produce change in family medicine practices attempting to adopt a new model of care. The authors analysed qualitative data generated by one facilitator and six practices by coding facilitator field notes, site visit reports, qualitative summaries, depth interviews and email strings. RESULTS: The facilitator utilized practice member coaching in addition to consulting, negotiating and connecting approaches. Coaching strategies encouraged: (i) expansive, multi-directional, attentive styles of communication; (ii) solving practical problems together; (iii) modelling facilitative leadership and (iv) encouraging an expanded vision of care. Practice members who received consistent coaching reported internal shifts and new ways of conceptualizing work, not just success at implementing model components. They indicated that their facilitator had helped them think and behave in new ways while helping them achieve benchmarks. CONCLUSIONS: It was once believed that the transition from traditional models of family medicine practice to new models of care meant implementing new technological components, suggesting that outside facilitators should act as technological and care delivery consultants. However, coaches may be especially useful in helpful in practices undertake substantial changes.
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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 | 0.008 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".