Are Health Care Professionals Prepared to Implement Human Papillomavirus Testing? A Review of Psychosocial Determinants of Human Papillomavirus Test Acceptability in Primary Cervical Cancer Screening
Bibliographic record
Abstract
Background: Guidelines for cervical cancer screening have been updated to include human papillomavirus (HPV) testing, which is more sensitive compared to cytology in detecting cervical intraepithelial neoplasia. Because of its increased sensitivity, a negative HPV test is more reassuring for a woman that she is at low risk for precancerous cervical lesions than a negative Pap test. Prompted by the inadequate translation of HPV test-based screening guidelines into practice, we aimed to synthesize the literature regarding health care providers (HCPs) knowledge, attitudes, and practices related to HPV testing and the influence of psychosocial factors on HCPs acceptability of HPV testing in primary cervical cancer screening. Materials and Methods: We searched MEDLINE, Embase, PsycINFO, CINAHL, Global Health, and Web of Science for journal articles from January 1, 1980 to July 25, 2018. A narrative synthesis of HCPs knowledge, attitudes, and practices related to HPV testing is provided. Informed by the Patient Pathway framework, we used deductive thematic analysis to synthesize the influence of psychosocial factors on HCPs acceptability of HPV testing. Results: The most important HCP knowledge gaps are related to the superior sensitivity of the HPV test and age-specific guideline recommendations for HPV testing. Thirty to fifty percent of HCPs are not compliant with guideline recommendations for HPV testing, for example, screening at shorter intervals than recommended. Barriers, facilitators, and contradictory evidence of HCPs' acceptability of the HPV test are grouped by category: (1) factors related to the HCP; (2) patient intrinsic factors; (3) factors corresponding to HCP's practice environment; and (4) health care system factors. Conclusions: HCP's adherence to guidelines for HPV testing in cervical cancer screening is suboptimal and could be improved by specialty organizations ensuring consistency across guidelines. Targeted educational interventions to address barriers of HPV test acceptability identified in this review may facilitate the translation of HPV testing recommendations into practice.
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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 | 0.010 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.008 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".