Female health-care providers’ advocacy of self-sampling after participating in a workplace program for cervical cancer screening in Ghana: a mixed-methods study
Bibliographic record
Abstract
BACKGROUND: Cervical cancer is the second most common cancer among Ghanaian women and screening coverage is low. ACCESSING is a cross-sectional study investigating human papillomavirus (HPV) prevalence via self-sampling in rural communities of the North Tongu district in Ghana. Female health-care providers (HCPs) were invited to self-collect a cervicovaginal sample with a commercial sampler in order to acquaint themselves with the sampling method. OBJECTIVE: This study set out to explore female HCPs' perceptions, advocacy for, and implications of self-sampling with the aim of enhancing self-sampling acceptability in the targeted screening population. METHODS: A mixed-methods approach was used, consisting of (a) a survey among 52 female HCPs working in a district hospital and (b) 10 one-to-one semi-structured interviews with purposefully sampled HCPs. RESULTS: The quantitative analysis of the survey (n = 52) showed that, among HCPs who took the sample themselves (50/52), all found it 'Easy' or 'Very Easy' and felt 'Very Comfortable' or 'Comfortable'. 82.7% indicated that they would undertake screening more often, and 98.1% indicated they would prefer self-sampling, if cervical cancer risk is as reliably determined as by clinician-directed cytobrush sampling. All interview participants (n = 10) indicated that they appreciated the program and would recommend the screening to their patients and/or family members and neighbours. Common reasons for preferring self-sampling were less (anticipated) pain compared to speculum examination and more privacy. CONCLUSIONS: Self-sampling for cervical cancer screening is highly acceptable to female HCPs. Setting up a workplace screening program that entails the option of self-sampling could create greater awareness and positive attitudes among HCPs to educating their patients, families, and neighbours on cervical cancer risks and motivate HCPs to advocate for women's participation in 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 | 0.009 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".