Stakeholders’ perspectives on barriers to and facilitators of school-based HPV vaccination in the context of COVID-19 pandemic-related disruption: a qualitative mixed methods study
Bibliographic record
Abstract
Despite successfully implementing the Human Papilloma Virus Vaccine (HPVV) program, Saskatchewan (SK) struggled to improve HPVV uptake rates. This suboptimal uptake of HPVV with a status quo of HPV-linked cervical cancer incidence rate is mainly because HPVV's impact on cancer prevention has not been realized adequately by vaccine providers and receivers. Further exploration of determinants of HPVV uptake is required to uncover high-resolution quality improvement targets for investment and situate contextually appropriate policies to improve its uptake. The study undertook a qualitative inquiry into understanding stakeholders' perspectives on HPVV experience through school-based programmes. It collected data through semi-structured initial interviews (N = 16) and follow-up interviews (N = 10) from across Saskatchewan's four Integrated Service Areas. Document analysis was conducted on all publicly available documents that included information on HPVV from January 2015 to July 2023. Thematic analysis of the data identified that inadequate information, awareness and education about HPV infection and HPVV among several groups, especially, parents, youth and school staff, was the main barrier to optimal HPVV uptake. Vaccine-related logistics, including the technical and text-heavy vaccine information sheet, understaffing, and time constraints, were other important factors that impeded HPVV uptake. A person-centred approach could educate parents in multiple dimensions.
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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 | 0.014 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| 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".