Barriers experienced by families new to Alberta, Canada when accessing routine-childhood vaccinations
Bibliographic record
Abstract
Abstract Background As Canada and other high-income countries continue to welcome newcomers, we aimed to 1) understand newcomer parents’ attitudes towards routine-childhood vaccinations (RCVs), and 2) identify barriers newcomer parents face when accessing RCVs in Alberta, Canada. Methods Between July 6th—August 31st, 2022, we recruited participants from Alberta, Canada to participate in moderated focus group discussions. Inclusion criteria included parents who had lived in Canada for < 5 years with children < 18 years old. Focus groups were transcribed verbatim and analyzed using content and deductive thematic analysis. The capability opportunity motivation behaviour model was used as our conceptual framework. Results Four virtual and three in-person focus groups were conducted with 47 participants. Overall, parents were motivated and willing to vaccinate their children but experienced several barriers related to their capability and opportunity to access RCVs. Five main themes emerged: 1) lack of reputable information about RCVs, 2) language barriers when looking for information and asking questions about RCVs, 3) lack of access to a primary care provider (PCP), 4) lack of affordable and convenient transportation options, and 5) due to the COVID-19 pandemic, lack of available vaccine appointments. Several minor themes were also identified and included barriers such as lack of 1) childcare, vaccine record sharing, PCP follow-up. Conclusions Our findings highlight that several barriers faced by newcomer families ultimately stem from issues related to accessing information about RCVs and the challenges families face once at vaccination clinics, highlighting opportunities for health systems to better support newcomers in 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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.009 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".