HPV Vaccine Communication Competency Scale for Medical Trainees: Interdisciplinary Development Study
Bibliographic record
Abstract
BACKGROUND: Human papillomavirus (HPV) infection is the most common sexually transmitted infection in the United States. High-risk HPV strains are associated with cancer of the cervix, oropharynx, anus, rectum, penis, vagina, and vulva. To combat increasing HPV-related cancers, the 9-valent HPV vaccine Gardasil was developed. Recommendation of the HPV vaccine by a health care provider has been cited as the number one factor affecting vaccine uptake among adolescents and young adults. Physician assistants, nurse practitioners, and pharmacists have been enlisted to bridge the gap. OBJECTIVE: The specific aim of this research study was to develop a reliable and valid HPV vaccine communication scale that can be used to measure the competency of primary care providers when recommending the need for vaccination to parents and patients. METHODS: Using a descriptive study, we collected data via a literature review, focus groups, and an expert panel to inform the scale domains and blueprint design. Pretesting (cognitive interviews) was used to inform item revision decisions. An item analysis was also conducted for the responses provided in the cognitive interviews. Item statistics (means and SDs), interitem correlations, and reliability were examined. Data were analyzed using SPSS (IBM Corp) software. RESULTS: A valid and reliable 42-item HPV vaccine communication competency scale was developed. The scale included 6 domains of interest. Scale items were moderately to strongly correlated with one another, and Cronbach α indicated good internal consistency with each scale. Scale items included were related to provider introduction or rapport (α=.796), patient respect or empathy (α=.737), provider interview or intake (α=.9), patient counseling or education (α=.935), provider communication closure (α=.896), and provider knowledge (α=.824). CONCLUSIONS: Pharmacists, nurse practitioners, and physician assistants should be trained to be competent in HPV vaccine communication and recommendation due to their expanded roles. Interdisciplinary collaboration is important to account for the trainee's individual differences and ensure the best health care outcomes for patients. A standardized HPV communication scale can be used to ensure effective and consistent recommendation by health care providers, thus affecting immunization rates.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".