Which primary care practitioners have poor human papillomavirus (HPV) knowledge? A step towards informing the development of professional education initiatives
Bibliographic record
Abstract
BACKGROUND: Primary care practitioners (PCP) play key roles in cervical cancer prevention. Human papillomavirus (HPV) knowledge is an important influence on PCPs' cervical cancer prevention-related behaviours. We investigated HPV knowledge, and associated factors, among general practitioners (GPs) and practice nurses. METHODS: A survey, including factual questions about HPV infection and vaccination, was mailed to GPs and practice nurses in Ireland. Multivariable logistic regression was used to determine which PCPs had low knowledge (questions correctly answered: infection ≤5/11; vaccination: ≤4/10). Questions least often answered correctly were identified. RESULTS: 697 PCPs participated. For HPV infection, GPs and practice nurses answered a median of nine and seven questions correctly, respectively (p<0.001). Significantly associated with low HPV infection knowledge were: being a practice nurse/male GP; working fewer hours/week; not having public patients; and having never taken a cervical smear. For HPV vaccination, both GPs and practice nurses answered a median of six questions correctly (p = 0.248). Significantly associated with low HPV vaccination knowledge were: being a practice nurse/male GP; working more years in general practice, fewer hours/week, in a smaller practice or in a practice not specialising in women's health; and having never taken a smear. Six HPV infection questions, and seven HPV vaccination questions, were not answered correctly by >⅓ of PCPs. CONCLUSIONS: There are important limitations in HPV infection and vaccination knowledge among PCPs. By identifying factors associated with poor knowledge, and areas of particular uncertainty, these results can inform development of professional education initiatives thereby ensuring women have access to uniformly high-quality HPV-related information and advice.
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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 | 0.015 | 0.054 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".