Barriers and facilitators of pap-smear test uptake in Asia: a systematic review
Bibliographic record
Abstract
BACKGROUND: In addition to the establishment of screening procedures, it is important to identify the barriers and facilitators for promoting preventive behavior. Many studies have been conducted in the field of investigating the factors affecting Pap smear test uptake and the barriers related to it. However, a systematic approach is still needed. Therefore, this present study was conducted with the aim of systematically reviewing the barriers and facilitators of Pap smear test uptake in Asia. METHODS: To collect the data, searches were performed in PubMed, WOS, ProQuest, Scopus and Cochrane databases from January 1, 2018 to January 15, 2025. Two people separately and independently evaluated the quality of the studies by Newcastle-Ottawa Scale. To conceptualize influential factors, barriers and facilitators of Pap-smear test uptake among Asian women, a theoretical thematic analysis was applied. RESULTS: A search yielded 4057 records, of which 44 documents discussing the determinants, barriers, and facilitators of Pap smear uptake were included in the review. There were economic, social, awareness, test and provider characteristics, and lifestyle and health behaviors dimensions in both categories of barriers and facilitators. In addition, two religious and psychological dimensions were included in the barriers category. In total, 55 components representing barriers and 51 components representing facilitators were identified. CONCLUSION: To improve Pap smear uptake, implement financial assistance and comprehensive insurance coverage. Enhance community engagement through outreach and support groups, provide counseling, and create positive messaging. Increase accessibility with mobile clinics, flexible hours, and train providers. Promote health education and offer incentives to motivate women to participate in screenings.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.013 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.011 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| 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".