A Qualitative Examination of Primary Care Team’s Participation in the Distribution of the COVID-19 Vaccination
Bibliographic record
Abstract
Abstract Background Primary health care (PHC) has historically led and implemented successful immunization programs, driven by strong relationships with patients and communities. During the COVID-19 pandemic, Canada began its vaccination strategy with mass immunizations that later included local efforts with PHC providers. This rollout approach has been criticized for not effectively utilizing the expertise of PHC to support vaccination distribution. This study seeks to understand how PHC contributed to the different phases of the COVID-19 vaccination rollouts in Ontario, Canada’s most populous province. Methods We conducted a descriptive qualitative study with focus groups consisting of PHC providers, administrators, and staff in Ontario. Eight focus groups were held with 39 participants representing geographic diversity across the six Ontario Health regions. Participants reflected a diverse range of clinical, administrative, and leadership roles. Each focus group was audio-recorded and transcribed with transcriptions analyzed using thematic analysis. Results With respect to understanding PHC teams’ participation in the different phases of the COVID-19 vaccination rollouts, we identified five themes: i) supporting long-term care, ii) providing leadership in mass vaccinations, iii) integrating vaccinations in PHC practice sites, iv) reaching those in need through outreach activities; and v) PHC’s contributions being under-recognized. Conclusions PHC was instrumental in supporting COVID-19 vaccinations in Ontario, Canada. The versatility of primary care enabled participation across all phases of Ontario’s COVID-19 vaccine rollout which enabled access to vaccines for the most vulnerable populations and communities. The flexibility and adaptability of PHC allowed teams to participate in both large-scale and small-scale vaccination efforts.
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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 | 0.014 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.015 | 0.014 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".