Examining policy cohesion for cervical cancer worldwide: analysis of WHO country reports
Bibliographic record
Abstract
INTRODUCTION: Cervical cancer is controllable through appropriate interventions such as vaccination, screening, treatment, early diagnosis and palliative care. The greatest burden of cervical cancer lies in low-income countries (LIC) where most of these services are missing or developed asymmetrically. Indeed, it is important to have not just an expansion, but a symmetric and concordant development of each service. Therefore, policies of countries should be aligned to provide concordant services and achieve the best outcomes with available resources. This is called 'policy cohesion' and for the first time in literature we will analyse cervical cancer policy coherence in all the 194 WHO member states. METHODS: The study is based on the 2017 WHO Non-Communicable Disease Country Capacity Surveys (NCD CCS). Although the survey covers multiple non-communicable diseases, in this report we will only discuss those results pertaining to cervical cancer, analysing the cervical cancer policy cohesion of 194 WHO member states, divided by WHO region and World Bank income group. RESULTS: Human papilloma virus vaccination exists in 53% of countries. 76% of countries offer cervical screening: among these countries, treatment, early diagnosis guidelines and palliative care are missing in 13%, 13% and 40%, respectively. In the African region, this discord is even more profound: 32%, 17% and 60%, respectively. CONCLUSION: Especially in those settings where resources are limited, early detection guidelines, treatment and palliative care should be implemented along with secondary prevention strategies. Symmetric development of concordant cervical cancer services maximises cervical cancer control efficacy.
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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 | 0.018 | 0.063 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.021 | 0.038 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".