Human papillomavirus vaccination intentions and uptake in college women.
Bibliographic record
Abstract
OBJECTIVE: Using the health belief model (HBM) and theory of planned behavior (TPB) as theoretical frameworks, the objectives of this study were: (a) to identify correlates of human papillomavirus (HPV) vaccination intentions and (b) to explore differences between correlates of HPV vaccination intentions and uptake. METHODS: Undergraduate women (N = 447) who did not intend to receive (n = 223), intended to receive (n = 102), or had received (n = 122) the HPV vaccine were surveyed. Logistic regressions were conducted to examine the correlates of vaccination intentions and uptake. RESULTS: Negative health consequences of the vaccine, physician's recommendation, positive attitudes toward the vaccine, and subjective norms were significant correlates of vaccination intentions. When comparing correlates of vaccination intentions to correlates of vaccination uptake, physician's recommendation, subjective norms, and perceived susceptibility to HPV were unique correlates of uptake. CONCLUSION: Differences between correlates of vaccination intentions and uptake suggest that social influences of liked and trusted individuals may make an important and unique contribution in motivating young women to receive the HPV vaccine beyond other variables from the HBM and TPB. Future utilization of longitudinal designs is needed to understand which factors may cause individuals to decide to receive the HPV vaccine.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".