Factors associated with human papillomavirus (HPV) test acceptability in primary screening for cervical cancer: A mixed methods research synthesis
Bibliographic record
Abstract
Primary screening for cervical cancer is transitioning from the longstanding Pap smear towards implementation of an HPV-DNA test, which is more sensitive than Pap cytology in detecting high-risk lesions and offers greater protection against invasive cervical carcinomas. Based on these results, many countries are recommending and implementing HPV testing-based screening programs. Understanding what factors (e.g., knowledge, attitudes) will impact on HPV test acceptability by women is crucial for ensuring adequate public health practices to optimize cervical screening uptake. We used mixed methods research synthesis to provide a categorization of the relevant factors related to HPV primary screening for cervical cancer and describe their influence on women's acceptability of HPV testing. We searched Medline, Embase, PsycINFO, CINAHL, Global Health and Web of Science for journal articles between January 1, 1980 and October 31, 2017 and retained 22 empirical articles. Our results show that while most factors associated with HPV test acceptability are included in the Health Belief Model and/or Theory of Planned Behavior (e.g., attitudes, knowledge), other important factors are not encompassed by these theoretical frameworks (e.g., health behaviors, negative emotional reactions related to HPV testing). The direction of influence of psychosocial factors on HPV test acceptability was synthesized based on 14 quantitative studies as: facilitators (e.g., high perceived HPV test benefits), barriers (e.g., negative attitudes towards increased screening intervals), contradictory evidence (e.g., sexual history) and no impact (e.g., high perceived severity of HPV infection). Further population-based studies are needed to confirm the impact of these factors on HPV-based screening acceptability.
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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 | 0.075 | 0.171 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.020 | 0.024 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".