The Role of Health Information Technology in Improving Awareness of Human Papillomavirus and Human Papillomavirus Vaccine Among U.S. Adults
Bibliographic record
Abstract
Objective: Although human papillomavirus (HPV) vaccines prevent cancer-causing HPV infections and cervical precancers, there is suboptimal awareness and limited global accessibility of HPV and HPV vaccine. Emerging evidence suggests that health information technology (HIT) may influence HPV-related awareness and improve vaccine adoption. The objective of this study was to evaluate the link between HIT and HPV-related awareness Methods: Data were obtained from 1,866 U.S. adults aged 18–45 years who completed the 2017 and 2018 Health Information National Trends Survey. We conducted multivariable logistic regression to analyze the association between HIT utilization and HPV-related awareness. Results: Awareness of HPV and HPV vaccine were 72.7% and 67.5%, respectively. Participants who used electronic means to look up health information (adjusted odds ratio [aOR] = 3.05; p = 0.001), communicate with health care provider (aOR = 1.68; p = 0.026), look up test results (aOR = 1.94; p = 0.005), and track health costs (aOR = 1.65; p = 0.04) were more likely to report HPV awareness than those who did not. Participants who used an electronic device to look up health information (aOR = 3.10; p = 0.003), communicate with clinicians (aOR = 1.72; p = 0.008), look up test results (aOR = 1.63; p = 0.021), and track health care charges (aOR = 1.90; p = 0.006) were more likely to report HPV vaccine awareness than those who did not. Discussion and Conclusion: Our findings suggest a positive association between HIT utilization and HPV-related awareness. Given the rapid and exponential increase in mobile technology access globally, these results are encouraging and offer a potential opportunity to leverage digital technology in primary cancer prevention for HPV-related cancers, especially in low- and middle-income countries with unsophisticated health infrastructures.
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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 | 0.004 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".