Addressing Vaccine Hesitancy in Canada: Paediatricians’ Perspectives and Social Work Opportunities
Bibliographic record
Abstract
The objectives of this three-paper thesis are to: (1) explore the prevalence and impact of parental vaccine hesitancy on paediatricians in Canada; and, (2) use a social work lens to examine vaccine hesitancy and identify novel approaches involving social work to enhance vaccine uptake for young children in Canada. This research is guided by four theoretical perspectives: The Health Belief Model, The Theory of Planned Behaviour, Ecological Systems Theory and The Transtheoretical Theory of Change Model. The first and second papers are based on survey data collected through the Canadian Paediatric Surveillance Program (CPSP). The first paper examined the frequency with which paediatricians encounter vaccine hesitancy in clinical practice, challenges and impacts of hesitancy on practice and factors associated with vaccine-compliance among parents with concerns about vaccination. The second paper examined factors predicting physician-reported parental intention to vaccinate following the diagnosis of a paediatric vaccine-preventable disease. The third paper explores how social workers could contribute to addressing vaccine hesitancy with respect to early childhood vaccines in Canada. The findings of this dissertation indicate that paediatricians encounter parents with concerns about vaccination frequently in clinical practice. The most significant predictor of vaccine compliance among parents of young children with concerns about vaccination was receiving a strong personal recommendation from their primary care physician. The only significant predictor of physician-reported parental intention to vaccination following the diagnosis of a paediatric vaccine-preventable disease was whether the parent previously refused all vaccines for the child. Vaccine hesitancy is a complex phenomenon which has traditionally remained within the purview of physicians. Social workers have the training and skills to effectively address vaccine hesitancy, particularly among families facing structural barriers to accessing vaccination. This dissertation concludes with a discussion of key findings, implications for social work and healthcare and suggestions for future research, policy and practice 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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.017 | 0.006 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".