A revolution in cervical cancer prevention in Ghana
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Though cervical cancer is largely preventable, success depends on sustained screening and treatment of precancer. This is not available in many low resource settings where screening and treatment services are not available due to a lack of government support. Our vision of setting up a comprehensive cervical cancer prevention scheme across Ghana that offers services tailored to fit every patient's needs, and relies on task shifting has been made possible through the setting up of the Cervical Cancer Prevention and Training Centre (CCPTC) to train and equip middle cadre staff (mostly nurses and midwives) to provide crucial cervical precancer screening and treatment services in many areas of the country that have never seen any such screening activities. To achieve this vision, we have learnt to produce crucial context relevant teaching materials and consumables locally, while adapting simple, readily available social media applications to raise crowd funds to support our work, use these apps to support routine work and to create a network of service providers at various service levels that can rely on each other and assure quality. Our vision has been supported by individuals and organizations that believe in it. They have allowed us to determine our growth and success. By sharing the experiences of the CCPTC we hope to encourage others to set up screening centers in low resource settings.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.037 | 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 it