Mother–Child Approach to Cervical Cancer Prevention in a Low Resource Setting: The Cameroon Baptist Convention Health Services Story
Bibliographic record
Abstract
INTRODUCTION: The rates of cervical cancer screening in Cameroon are unknown and HPV vaccination coverage for age-appropriate youths is reported at 5%. OBJECTIVES: To implement the mother-child approach to cervical cancer prevention (cervical screening by HPV testing for mothers and HPV vaccination for daughters) in Meskine, Far North, Cameroon. METHODS: After the sensitization of the Meskine-Maroua region using education and a press-release by the Minister of Public Health, a 5-day mother-child campaign took place at Meskine Baptist Hospital. The Ampfire HPV Testing was free for 500 women and vaccination was free for age-appropriate children through the EPI program. Nurses trained in cervical cancer education conducted group teaching sessions prior to having each woman retrieve a personal sample. Self-collected samples were analyzed for HPV the same day. All women with positive tests were assessed using VIA-VILI and treated as appropriate for precancers. RESULTS: 505 women were screened, and 92 children vaccinated (34 boys and 58 girls). Of those screened, 401 (79.4%) were aged 30-49 years old; 415 (82%) married; 348 (69%) no education. Of the HPV positive cases (101): 9 (5.9%) were HPV 16, 11 (10.1%) HPV 18, 74 (73%) HPV of 13 other types. Those who were both HPV and VIA-VILI positive were treated by thermal ablation (63%) or LEEP (25%). CONCLUSION: The mother-child approach is an excellent method to maximize primary and secondary prevention against cervical cancer.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.018 | 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".