Acceptability and preferences for self‐collected screening for cervical cancer within health systems in rural Uganda: A mixed‐methods approach
Bibliographic record
Abstract
OBJECTIVE: To understand the knowledge, preferences, and barriers for self-collected cervical cancer screening (SC-CCS) and follow-up care at the individual and health system level to inform the implementation of community-based SC-CCS. METHODS: Surveys and focus group discussions (FGDs) with women and FGDs with healthcare providers were conducted in Uganda. Survey data were analyzed using frequencies and FGD data were analyzed using thematic content analysis. Data were triangulated between methods. RESULTS: Sixty-four women were surveyed and 58 participated in FGDs. Facilitators to screening access included decentralization, convenience, privacy, confidentiality, knowledge, and education. Barriers to accessing screening included lack of transportation and knowledge, long wait times, difficulty accessing health care, and lack of trust in the health system. Additional implementation challenges included insufficiently trained human resources and lack of infrastructure. CONCLUSION: Integrating SC-CCS within rural health systems in low-resource settings has been under-evaluated. Community-based SC-CSS could prevent high cervical cancer-related mortalities while working within the human and financial resource limitations of rural health systems. SC-CCS is acceptable to women and healthcare providers. By addressing rural women's preferences and barriers to care, decision-makers can build health systems that provide community-centered care close to women's homes across the care continuum.
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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 | 0.020 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".