Health coaching in primary care: a feasibility model for diabetes care
Bibliographic record
Abstract
BACKGROUND: Health coaching is a new intervention offering a one-on-one focused self-management support program. This study implemented a health coaching pilot in primary care clinics in Eastern Ontario, Canada to evaluate the feasibility and acceptability of integrating health coaching into primary care for patients who were either at risk for or diagnosed with diabetes. METHODS: We implemented health coaching in three primary care practices. Patients with diabetes were offered six months of support from their health coach, including an initial face-to-face meeting and follow-up by email, telephone, or face-to-face according to patient preference. Feasibility was assessed through provider focus groups and qualitative data analysis methods. RESULTS: All three sites were able to implement the program. A number of themes emerged from the focus groups, including the importance of physician buy-in, wide variation in understanding and implementing of the health coach role, the significant impact of different systems of team communication, and the significant effect of organizational structure and patient readiness on Health coaches' capacity to perform their role. CONCLUSIONS: It is feasible to implement health coaching as an integrated program within small primary care clinics in Canada without adding additional resources into the daily practice. Practices should review their organizational and communication processes to ensure optimal support for health coaches if considering implementing this intervention.
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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 | 0.033 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".