Practice Facilitation for Family Physicians to Contact Patients Unvaccinated for COVID-19: A Process Evaluation
Bibliographic record
Abstract
Context: One way to build vaccine confidence is through advice from a trusted health professional, like a family physician. We implemented and evaluated a practice facilitation intervention in partnership with Ontario Health, a provincial government agency, to support the family physicians in Ontario known to have the largest number of unvaccinated patients. Objective: To understand how and why this intervention offered to family physicians most in need was used and to identify opportunities to adapt this targeted approach for other aspects of primary care. Study Design and Analysis: This was a qualitative process evaluation embedded within a randomized trial. Data were analyzed using inductive and deductive techniques informed by the Theoretical Domains Framework. Setting or Dataset: Data was collected through interviews with participating and non-participating family physicians, as well as from practice facilitator field notes. Population Studied: Family physicians with the largest number of unvaccinated patients in the province. Intervention/Instrument: Practice facilitators offered physicians support to identify, reach out, and counsel their unvaccinated patients. Outcome Measures: N/A Results: Of the 300 physicians randomized to the intervention, 29% accepted support. Among those who received facilitation, 58% used technical support to identify unvaccinated patients, 29% were linked with medical student volunteers to contact patients on their behalf, and 31% used automated calling to reach patients. The main barriers encountered were: a) gatekeeping from the front-office staff, which prevented the practice facilitator from contacting the physician, b) inability to dedicate extra time, c) technical issues with identifying patients requiring vaccines, and d) belief that outreach to patients would not result in more vaccinations. Facilitators were: a) clinics that had support staff, especially a tech-savvy workforce, b) physician, or the team passionate about COVID-19 vaccine uptake, and therefore willing to prioritize time or resources to outreach, and c) the belief that COVID-19 vaccine uptake was part of their role as a family physician. Conclusion: Strategies to help family physicians regarding vaccine rollout or other primary care areas of public health import must acknowledge the scarcity of time and resources of these physicians, and seek to build trust and relationships over time, including the front-office staff.
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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 | 0.070 | 0.095 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".