International barriers and facilitators for cervical cancer screening among young women: a systematic review
Bibliographic record
Abstract
Background Though cervical cancer is one of the leading causes of death globally, its incidence is nearly entirely preventable. Young women have been an international priority for screening. However, in both high-income and low-income countries, young women have not been screened appropriately according to country-specific guidelines and in many countries, screening rates for this age-group have even dropped. Objectives The aim of this systematic review was to systematically characterize the existing literature on barriers and facilitators for cervical cancer screening among young women globally. Search Strategy We conducted a systematic review following PRISMA guidelines of four databases: Medline-OVID, EMBASE, CINAHL, and ClinicalTrials.Gov. Selection Criteria We only examined original, peer-reviewed literature. Databases were examined from inception until the date of our literature searches (12/03/2020). Articles were excluded if they did not specifically discuss cervical cancer screening, were not specific to young women, or did not report outcomes or evaluation. Data Collection and Analysis All screening and extraction was completed in duplicate with two independent reviewers. Main Results Of the 2177 original database citations, we included 36 studies that met inclusion criteria. Our systematic review found that there are three large categories of barriers for young women: lack of knowledge/awareness, negative perceptions of the test, and practical barriers to testing. Facilitators included stronger relationships with healthcare providers, social norms, support from family, and self-efficacy. Conclusions Health systems worldwide should address the barriers and facilitators to increase cervical cancer screening rates in young women. Further research is required to understand this age group.
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 | 0.021 | 0.099 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.009 | 0.007 |
| Bibliometrics | 0.014 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".