Working towards health: A model of cervical cancer screening and treatment for factory employees in Haiti
Bibliographic record
Abstract
OBJECTIVE: In Haiti, cervical cancer continues to cause high levels of mortality and morbidity due to lack of resources and political unrest. Haitian women employed in factories are especially vulnerable because they are unable to take time away from work to access health resources. We aimed to describe a low-cost intervention which successfully addressed this need. METHODS: We present a retrospective review of data gathered through a public-private partnership, in which women working in garment factories near Port-au-Prince, Haiti, were offered health education, clinical breast exam, and free human papillomavirus (HPV) self-swab testing at their place of employment. Women testing positive for HPV were subsequently tested using visual inspection with acetic acid (VIA) to inform treatment referrals, and treated with mobile thermocoagulation in factory infirmaries. Factory-employed healthcare workers were trained on cancer screening, including VIA and clinical breast exam. RESULTS: A total of 6843 out of 6983 (98%) female factory employees attended free reproductive health education sessions, and 4005 out of 4153 eligible women (97%) were screened using HPV self-swab testing; 5176 women received a clinical breast exam. Of the women screened for HPV, 1001 (25%) tested positive and 905 (90%) of HPV-positive women received VIA testing and thermocoagulation. The intervention had a total cost of US$76 000, over half of which was spent on an HPV testing machine. CONCLUSIONS: Innovative approaches to the prevention of cervical cancer are especially necessary in very low-resource, politically unstable environments like Haiti. Self-swab and screen-and-treat programs in the workplace were acceptable to employees and factory owners. This low-cost model was reached vulnerable women through a public-private partnership, and tracked them through screening and treatment. It could be implemented elsewhere or extended to include other health services.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".