Barriers and supports for uptake of human papillomavirus vaccination in Indigenous people globally: A systematic review
Bibliographic record
Abstract
Despite the availability of effective and safe human papillomavirus (HPV) vaccines that reduce the incidence and impact of cervical cancer and other cancers, HPV vaccine coverage rates remain persistently low and the cervical cancer burden disproportionately high among Indigenous people globally. This study aimed to systematically identify, appraise, and summarize the literature on documented barriers and supports to HPV vaccination in Indigenous populations internationally. Forty-three studies were included and an inductive, qualitative, thematic synthesis was applied. We report on 10 barrier themes and 7 support themes to vaccine uptake, and provide a quantitative summary of metrics. Focusing on Indigenous perspectives reported in the literature, we propose recommendations on community-research collaboration, culturally safe intergenerational and gender-equitable community HPV vaccine education, as well as multi-level transparency to ensure informed consent is secured in the context of reciprocal relationships. Although the voices of key informant groups (e.g., HPV-vaccine eligible youth and community Elders) are underrepresented in the literature, the identification of barriers and supports to HPV vaccination in a global Indigenous context might help inform researchers and health policy makers who aim to improve HPV vaccine uptake in Indigenous populations.
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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 | 0.009 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".