Modelling the Impacts of Trust, Behaviour, and Social Determinants of Health on HPV Vaccination: A Simulation Study
Bibliographic record
Abstract
Human papillomavirus (HPV) vaccination is a critical public health intervention for preventing HPV infection and subsequently HPV-related cancers. Vaccination uptake is influenced by complex interactions between social determinants of health (SDOH), trust in healthcare, and individual decision-making. This study developed and calibrated an agent-based model to explore these dynamics in Saskatchewan, Canada. The model incorporated demographic data from recent census records and psychosocial factors, including trust, knowledge, and health belief model components, informed by empirical literature. Calibration involved comparing model outputs to real-world data and validating emergent properties, such as baseline vaccination rates and parental knowledge levels. Scenario analyses examined the impact of interventions, including increasing trust and knowledge, implementing digital consent forms, and expanding catch-up vaccination programs. Results demonstrated that catch-up series, and digital consent forms significantly influenced vaccination uptake. The increased form return time intervention further showed promising results. The model serves as a tool for policy makers and researchers beyond the scope of this study, as its iterative nature lends well to being integrated within other health research practices. The results highlight the need for a multi-faceted approach to improve HPV vaccination rates, addressing both psychosocial and logistical barriers.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".