Practice Facilitation to Support Family Physicians in Encouraging COVID-19 Vaccine Uptake: A Multimethod Process Evaluation
Bibliographic record
Abstract
PURPOSE: We offered a practice facilitation intervention to family physicians in Ontario, Canada, known to have large numbers of patients not yet vaccinated against coronavirus disease 2019 (COVID-19). METHODS: We conducted a multimethod process evaluation embedded within a randomized controlled trial (clinical trial #NCT05099497). We collected descriptive statistics regarding engagement and qualitative interview data from family physicians and practice facilitators, as well as data from facilitator field notes. We analyzed and triangulated the data using thematic analysis and mapped barriers to and enablers for implementation to structural, organizational, physician, and patient factors. RESULTS: Of the 300 approached, 90 family physicians (30%) accepted facilitation. Of these, 57% received technical support to identify unvaccinated patients, 29% used trained medical student volunteers to contact patients on their behalf, and 30% used automated calling to reach patients. Key factors affecting engagement with the intervention were staff shortages owing to COVID-19 (structural), clinic characteristics such as technical issues and gatekeeping by staff, which prevented facilitators from talking with physicians (organizational), burnout (physician), and specialized populations that required targeted resources (patient). The facilitator's ability to address technical issues and connect family physicians with medical students helped with engagement. CONCLUSIONS: Strategies to help underresourced family physicians serving high-needs populations for issues of public health importance, such as vaccine promotion, must acknowledge the scarcity of physicians' time and provide new resources. To successfully engage family physicians, practice facilitators should seek to build trust and relationships over time, including with 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 | 0.058 | 0.059 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".