<i>“The needle is already ready to go”</i> : communities’ and health care professionals’ perceptions of routine vaccination in Nunavik, Canada
Bibliographic record
Abstract
Inuit living in the northern region of Nunavik continue to experience significant health inequalities, which are rooted in colonialism that still have repercussions on their health-related perceptions and practices, including vaccination. This study aimed to explore the perceptions and determinants of routine vaccination among the Inuit of Nunavik by describing factors influencing vaccination decisions from the perspective of community members and health professionals. Semi-structured interviews focusing on the perception of vaccination and experience with vaccination and health services were conducted with 18 Inuit and 11 non-Inuit health professionals. Using the socio-ecological model, factors acting at the community and public policy (e.g. rumours and misinformation about vaccination, language barrier), organisational (e.g. complexity of the vaccination process, staff turnover, lack of specialised vaccination workers and interpreters), and intrapersonal and interpersonal (e.g. past experiences with vaccination, vaccine attitudes, social norms) levels were identified as having an impact on vaccination decisions. Improving vaccination coverage in Nunavik requires a more global reflection on how to improve and culturally adapt the health care and services offered to the Inuit population.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.005 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".