Expanding access to HPV screening through community health insurance schemes: lessons from a screening exercise for teachers in Ghana
Bibliographic record
Abstract
BACKGROUND: Cervical cancer (CC) screening uptake remains low primarily owing to the absence of organized screening and lack of insurance coverage. Members of the Ghana National Association of Teachers (GNAT) contribute monthly to an insurance scheme which covers cancer (including CC) treatment but not cervical precancer screening/treatment. We conducted this study to examine health beliefs shaping cervical screening uptake among educators and to understand how the scheme could scale cervical precancer screening and treatment services for beneficiaries across the country. METHODS: From February − July 2022, we performed cervical precancer screening with concurrent hr-HPV DNA testing and visual inspection with acetic acid (VIA) for 102 teachers in 3 districts in the Volta Region. From April − May 2024, we conducted a cross-sectional study among 498 female subscribers of the GNAT insurance scheme in the same districts using non-probability snowball sampling. Data on utilization of cervical cancer screening services and risk factors were collected using a self-administered questionnaire. Health beliefs and situational factors associated with screening uptake among school teachers were assessed. RESULTS: Although all 498 female school teachers were enrolled in the cancer insurance scheme, cervical cancer screening uptake was reported by 116 (23.9%). Utilization of cervical cancer screening services was 25.3% among married women and 34.4% among women who reported limited access to screening. In the final adjusted logistic regression model, perceived barriers to screening (aOR, 0.55; 95% CI, 0.42 − 0.72) and being divorced/widowed (aOR, 2.11; 95% CI, 1.10–4.03 vs. married/cohabitating) were independently associated with cervical precancer screening uptake. The hr-HPV prevalence and VIA ‘positivity’ rate were 17.3% (95% CI, 9.9–24.8) and 1.0% (95% CI, 0.0–5.5), respectively. CONCLUSIONS: Cervical precancer screening utilization among female teachers enrolled in the GNAT cancer insurance scheme was sub-optimal owing to barriers related to low awareness, limited access, and social factors. The cancer insurance scheme represents a golden opportunity to overcome the identified barriers and improve HPV screening access and outcomes in addition to increasing access to cervical cancer treatment and should be explored.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| 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".