Cervical cancer prevention in Burkina Faso: a stakeholder’s collaboration for the development of awareness messaging
Bibliographic record
Abstract
Background: Cervical Cancer stands as the second leading cause of both incident female cancers and deaths in Burkina Faso. Unfortunately, the prevention, early detection, and care of cervical cancers are suboptimal at individual, institutional, and national levels. In October 2023, we organized a stakeholder's workshop to develop cervical cancer awareness messaging for disease control in the country. Methods: A one-text workshop was organized with stakeholders working toward improving health in general or women's health and well-being. A participatory, learning, and adaptive approach was used to facilitate discussions and activities, ensuring the contribution of all participants. Contextual evidence-based and empirical elements about cervical cancer burden and preventive strategies were presented to the participants by key informants. These served as the foundation for a collaborative formulation of messaging content that aimed at raising awareness about cervical cancer. Results: Sixty-two participants from 28 organizations attended the workshop. They work mainly at local and international non-governmental organizations, civil society organizations, universities, university hospitals, research centers, and the Ministry of Health. During the first and second days of the workshop, the participants explored cervical cancer data, its preventive and treatment options available in Burkina Faso, communication strategies for behavioral change, and determinants of the use of prevention and health promotion services. During the following three days, 3 working groups were formed to define strategies, and key messages adapted to diverse tools and targeted audiences. All information was validated during plenary sessions before the end of the workshop and available to all participants and their organizations for cancer awareness activities. Conclusion: Upon conclusion of the workshop, the participants provided insightful information for the development of cervical awareness messaging in Burkina Faso. They formed the first community of practice to serve as a dynamic platform for implementation, monitoring, evaluation, and continued learning activities.
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 | 0.038 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".