Cervical cancer knowledge and barriers and facilitators to screening among women in two rural communities in Guatemala: a qualitative study
Bibliographic record
Abstract
BACKGROUND: Approximately 80% of deaths due to cervical cancer occur in low- and middle-income countries. In Guatemala, limited access to effective screening and treatment has resulted in alarmingly high cervical cancer incidence and mortality rates. Despite access to free-of-cost screening, women continue to face significant barriers in obtaining screening for cervical cancer. METHODS: In-depth interviews (N = 21) were conducted among women in two rural communities in Guatemala. Interviews followed a semi-structured guide to explore knowledge related to cervical cancer and barriers and facilitators to cervical cancer screening. RESULTS: Cervical cancer knowledge was variable across sites and across women. Women reported barriers to screening including ancillary costs, control by male partners, poor provider communication and systems-level resource constraints. Facilitators to screening included a desire to know one's own health status, conversations with other women, including community health workers, and extra-governmental health campaigns. CONCLUSIONS: Findings speak to the many challenges women face in obtaining screening for cervical cancer in their communities as well as existing facilitators. Future interventions must focus on improving cervical cancer-related knowledge as well as mitigating barriers and leveraging facilitators to promote screening.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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".