Exploring Community Perspectives on Barriers and Facilitators to Childhood Immunization among First Nations Peoples in Northern Saskatchewan: A Qualitative Sharing Circle Study
Bibliographic record
Abstract
Abstract Childhood vaccination is vital for disease prevention intervention, yet some Northern Saskatchewan First Nations communities have immunization coverage below the 95% target. We aimed to explore community perspectives on the barriers and facilitators of childhood immunization uptake in First Nations communities in Northern Saskatchewan. We used a qualitative design informed by Indigenous methodologies and community-based participatory approaches to engage three First Nations communities and conducted sharing circles to gather rich, culturally grounded perspectives on childhood immunization. We purposefully recruited parents, caregivers, an Elder, and a community leader. We audio-recorded and transcribed one sharing circle (5 participants) discussion, then analyzed data thematically with Indigenous research principles, ensuring community collaboration and ethical stewardship throughout the study. The results were validated through a community feedback session. Participants described several structural and social factors that limited access to immunization, including transportation barriers, limited clinic hours, geographic isolation, and reduced services during the COVID-19 pandemic. Caregiver mental health issues, substance use, and unstable home environments were identified as barriers to immunization uptake. Language barriers, lack of health literacy, and historical trauma also shaped vaccine hesitancy. Key enablers identified included establishining trusting relationships with local healthcare providers, use of mobile clinics, peer support, tailored communication strategies, community events that incorporated immunization programs rooted in cultural practices, and involvement of Elders and local leadership in planning and delivery. Our findings show that vaccine uptake in Northern Saskatchewan First Nations communities reflects a complex set of contextual factors, shaped by structural conditions and historical experiences. We recommend policy and practice approaches that support culturally safe, community-led immunization programming. Strengthening trust, expanding flexible service models, and integrating First Nations knowledge systems into public health planning can help improve childhood vaccination coverage and contribute to health equity in northern and remote settings.
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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 | 0.006 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.014 | 0.007 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".