Vaccine hesitancy among Syrian refugee parents in Canada: A multifaceted challenge in public health
Bibliographic record
Abstract
Vaccine hesitancy presents a significant public health challenge, particularly among Syrian refugee parents in Canada, who navigate unique barriers to vaccination. This cross-sectional study explores the determinants of vaccine hesitancy, considering socio-demographic factors, resettlement conditions, health assessments, and healthcare system interactions. The study involved 540 Syrian refugee parents residing in Ontario with at least one child under 18, interviewed from March 2021 to March 2022. Participants were asked about their willingness to take the COVID-19 vaccine, with those uncertain or unwilling categorized as "Hesitant" and others as "Non-hesitant." Stepwise multivariable logistic regression assessed various factors associated with vaccine hesitancy. Among respondents, 15.2% expressed hesitancy toward taking the COVID-19 vaccine. Findings indicated that individuals who reported very good or good mental health had decreased odds of being vaccine hesitant (OR = 0.46, 95% CI: 0.27-0.80). Also, individuals without a family doctor and those needing an interpreter but sometimes or never offered one were more likely to be hesitant (OR = 3.61, 95% CI: 1.42, 9.19; OR = 2.14, 95% CI: 1.19-3.84, respectively). These results highlight the complex interplay of factors affecting vaccine decisions, emphasizing the need for culturally sensitive public health strategies to improve vaccine uptake in this population. While vaccine acceptance is low among Syrians (36%), the higher rate among Syrian refugees in Canada (84.7%) reflects the positive impact of healthcare access and resettlement support. This contrast highlights the role of such systems in shaping vaccine attitudes among vulnerable populations, informing targeted public health efforts to boost vaccine acceptance and support the health of Syrian refugees.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".