A qualitative examination of the experiences and perspectives of interprofessional primary health care teams in the distribution of the COVID-19 vaccination in Ontario, Canada
Bibliographic record
Abstract
BACKGROUND: Primary health care (PHC) teams contributed to all phases of the COVID-19 vaccination distribution. However, there has been criticism for not fully utilizing the expertise and infrastructure of PHC teams for vaccination distribution. Our study sought to understand the role PHC teams had in the distribution of the COVID-19 vaccine in Ontario, Canada. The key objective informing this study was to explore the experiences and perspectives of interprofessional PHC teams in the distribution of COVID-19 vaccination across Ontario. METHODS: A qualitative approach was used for this study, which involved 39 participants from the six health regions of the province. Eight focus groups were conducted with a range of interprofessional healthcare providers, administrators, and staff working in PHC teams across Ontario. The sample reflected a diverse range of clinical, administrative, and leadership roles in PHC. Focus groups were audio-recorded and transcribed, while transcriptions were then analyzed using thematic analysis. RESULTS: We identified the following four themes in the data: i) PHC teams know their patients; ii) mobilizing team capacity for vaccination, iii) intersectoral collaborations, and iv) operational challenges. CONCLUSIONS: PHC teams were an instrumental component in supporting COVID-19 vaccinations in Ontario. The involvement of PHC in future vaccination efforts is key but requires additional resourcing and inclusion of PHC in decision-making. This will ensure provider well-being and maintain collaborations established during COVID-19 vaccination.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.025 | 0.013 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 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".