Addressing Barriers Newcomer Families Face When Obtaining Routine Childhood Vaccines in Alberta, Canada
Bibliographic record
Abstract
Background/Objectives: As the newcomer population in Canada continues to grow, we aimed to collaborate with newcomer families arriving in an urban center in Alberta, Canada to identify strategies to overcome identified barriers newcomers face in obtaining routine childhood vaccines (RCVs). Methods: We recruited newcomers living in Calgary, Alberta to participate in a workshop utilizing the Nominal Group Technique (NGT) to develop solutions addressing barriers to obtaining RCVs. Ranking exercises helped identify the top-proposed interventions based on perceived impact and feasibility for implementation. Based on the identified need for translated vaccine resources, infographics on school-based vaccines were developed. The infographics were pilot-tested in a first-language focus group before the final product was translated into 10 different languages. Results: Consensus from 15 NGT workshop participants identified five key solutions to facilitate obtaining routine childhood immunizations: (1) Increasing access to reliable vaccine information; (2) Ensuring vaccine information and healthcare services are available in different languages; (3) Increasing vaccine appointment availability and optimizing the booking system for ease of navigation; (4) Increasing the role of family doctors in vaccine counseling and administration; (5) Streamlining vaccine record tracking. We developed infographics on the vaccines children in Alberta can receive through school-based vaccine programs and these were pilot-tested with 16 participants in a first-language (Arabic) focus group. Conclusions: The collaborative and iterative process of solution development with newcomers provided a platform for knowledge translation through the development of educational resources on school-based vaccines, addressing the information barrier that newcomers identified when accessing RCVs.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".