Awareness regarding human papillomavirus and willingness for vaccination among college students with or without medical background in Guizhou Province
Bibliographic record
Abstract
This cross-sectional epidemiological study aimed to investigate awareness regarding human papillomavirus (HPV) and willingness for vaccination among college students with or without medical background in Guizhou Province, China. A logistic regression model was used for univariate and multivariate analyses of cognition to determine factors influencing willingness for vaccination. In total, 2,540 questionnaires were collected, of which 2,360 were valid. The medical and nonmedical groups included 737 (31.2%) and 1,623 (68.8%) individuals, respectively. The medical group had heard of HPV and its vaccines more frequently than the nonmedical group, with the former also having greater awareness than the latter (P < .001). Females (1,325, 56.1%) had heard of HPV and its vaccines more frequently than males (1,035, 43.9%), with the former also having greater awareness than the latter. The cost, safety, and efficacy of the HPV vaccine and lack of knowledge regarding HPV and its vaccines were the main barriers for vaccination. The overall level of knowledge regarding HPV and its vaccines was lower in the nonmedical group and males than in the medical group and females. To help promote willingness for vaccination among the nonmedical group and males, strategies that deepen their knowledge regarding the HPV vaccine are needed.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".