Facilitators and Challenges Emerging from Primary Care Teams’ Engagement in COVID-19 Vaccination Distribution in Ontario, Can
Bibliographic record
Abstract
Context: Primary care has historically established itself as an important part of vaccinations efforts due to its successful delivery of flu and childhood immunisation programmes centred on counselling and strong infrastructure. An effective and efficient distribution of a vaccine was essential for the recovery from the COVID-19 pandemic. Although primary care teams contributed to all phases of the COVID-19 vaccination distribution, involvement of primary care teams in Ontario, Canada has been inconsistent. Criticisms have emerged regarding the limited utilization of the expertise primary care in informing and guiding implementation of vaccinations. An increased understanding of the role primary care teams had in the distribution of the COVID-19 vaccines in Ontario will help determine the unique experiences of interprofessional primary care providers. Objective: To identify facilitators and challenges of integrating COVID-19 vaccination in interprofessional primary health care teams across Ontario. Study Design: Descriptive, qualitative focus groups conducted with interprofessional primary care providers and staff in Ontario. Results: We conducted 8 focus groups with 39 participants representing geographic diversity across Ontario. Participants reflected a range of clinical, administrative, and leadership roles. Three themes were identified as facilitators: i) primary care knows patients; ii) team capacity for vaccination, iii) intersectoral collaborations, and three themes identified challenges including: i) operational challenges, ii) impact on routine patient care, and iii) primary care being overlooked. Participants noted the importance of supporting provider well-being during a busy period of managing competing health needs of patients. In addition, many primary care teams either strengthened existing partnerships or collaborated with new stakeholders which was seen as important to maintain after the COVID-19 vaccinations. Conclusions: Primary care teams played a crucial role in supporting COVID-19 vaccinations in Ontario. Future vaccination efforts will benefit from the inclusion and involvement of primary care from the beginning to guide decision-making, along with additional resourcing.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.017 | 0.005 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".