Physicians’ perspectives on COVID-19 vaccinations for children: a qualitative exploration in Ontario, Canada
Bibliographic record
Abstract
OBJECTIVES: Parents' decisions to vaccinate their children against COVID-19 are complex and often informed by discussions with primary care physicians. However, little is known about physicians' perspectives on COVID-19 vaccinations for children or their experiences counselling parents in their decision-making. We explored physicians' experiences providing COVID-19 vaccination recommendations to parents and their reflections on the contextual factors that shaped these experiences. DESIGN: We conducted an interpretive qualitative study using in-depth interviews. We analyzed the data using reflexive thematic analysis and a socioecological framework. SETTING: This study involved primary care practices associated with The Applied Research Group for Kids (TARGet Kids!) primary care research network in the Greater Toronto Area, Ontario, Canada. PARTICIPANTS: Participants were 10 primary care physicians, including family physicians, paediatricians and paediatric subspecialists. RESULTS: Participants discussed elements at the individual level (their identity, role, and knowledge), the interpersonal level (their relationships with families, responsiveness to parents' concerns, and efforts to build trust) and structural level (contextual factors related to the evolving COVID-19 climate, health system pandemic response, and constraints on care delivery) that influenced their experiences providing recommendations to parents. Our findings illustrated that physicians' interactions with families were shaped by a confluence of their own perspectives, their responses to parents' perspectives, and the evolving landscape of the broader pandemic. CONCLUSIONS: Our study underscores the social and relational nature of vaccination decision-making and highlights the multiple influences on primary care physicians' experiences providing COVID-19 vaccination recommendations to parents. Our findings offer suggestions for future COVID-19 vaccination programmes for children. Delivery of new COVID-19 vaccinations for children may be well suited within primary care offices, where trusting relationships are established, but physicians need support in staying knowledgeable about emerging information, communicating available evidence to parents to inform their decision-making and dedicating time for vaccination counselling.
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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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.019 | 0.009 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.004 |
| 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".