The influence of health service interactions and local policies on vaccination decision-making in immigrant women: A multi-site Canadian qualitative study
Bibliographic record
Abstract
OBJECTIVES: Research on immigrant and refugee vaccination uptake in Canada shows that immunization decisions vary by vaccine type, location, age and migration status. Despite their diversity, these studies often treat immigrant and refugee populations as a single group relative to other Canadians. In this comparative study, we explored how previous risk communication and immunization experiences influence immunization decisions by immigrant and refugee women from three communities across Canada. METHODS: Participants included women from the Punjabi immigrant community located in Surrey and Abbotsford, British Columbia (n = 36), the Nigerian immigrant community located in Winnipeg, Manitoba (n = 43), and the Congolese refugee community in Edmonton, Alberta (n = 18). Using focus groups guided by focused ethnography methodology, we sought to understand immunization experiences in Canada and before arrival, and what information sources influenced the immunization decision-making process by the women in the three communities. RESULTS: Participants had differing past experiences in Canada and before their arrival that influenced how they used information in their vaccination decisions. Clear vaccination communications and dialogue with Canadian health care providers increased trust in Canadian health care and the likelihood of vaccine uptake. By contrast, weak vaccine recommendations and antivaccination information in the community prompted participants to decline future vaccines. CONCLUSION: Given our participants' different communication preferences and needs, we argue that a one-size-fits-all communication approach is inappropriate for immigrant and refugee populations. Instead, multi-pronged communication strategies are required to reach participants and respond to previous experiences and information that may lead to vaccination hesitancy.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.018 | 0.006 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.004 |
| 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".