A population‐based combination strategy to improve the cervical cancer screening coverage rate in Bamako, Mali
Bibliographic record
Abstract
INTRODUCTION: Cervical cancer screening coverage rate is <5% in Sub-Saharan Africa and <2% in French- speaking African countries. In 2016, we implemented strategies to improve cervical cancer screening in Bamako, the "Weekend70 program". The present study objectives are to determine the effect of this program on women's participation in cervical cancer screening in Bamako, and to estimate the cervical cancer screening coverage rate in Bamako. MATERIAL AND METHODS: From 1 January 2016 to 31 July 2020, we conducted an operational research by developing several strategies to improve the cervical cancer screening coverage rate among adolescents and women ≥15 years old in Bamako, Mali. The strategies consisted of awareness-raising activities, strengthening of screening practices in healthcare facilities and cost-free cervical cancer screening during the weekend. Descriptive statistics were presented. The cervical cancer coverage rate was calculated by dividing the number of women screened by the total number of women ≥20 years old, based on Mali demographic data. RESULTS: The total number of women screened was 289 924. Residents from Bamako represented 91.9% (266 436/289 924) vs 8.1% (23 488/289 924) who lived outside Bamako. The mean age was 33.2 (± 11.5) years old. Around 46.1% of participants attending the cervical cancer screening were between 30 and 49 years old (World Health Organization prioritized target age for cervical cancer screening). Women ≥60 years old represented <5%. Cervical cancer screening participation increased significantly, from <800 women screened per week before the implementation of the program to a peak of 4100 women screened per week during the "Weekend70 program". Overall, the cervical cancer screening coverage rates at the end of the study among women ≥20 years old was 47.3%, and 56.9% in the WHO target population. CONCLUSION: In an impoverished context, a multi-component strategy significantly increases cervical cancer screening participation.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".