Cancer Control in Developing Countries: Need for Epidemiological Surveillance based on Health Information Systems and Health Services Research
Bibliographic record
Abstract
The purpose of this paper is to present an argument for the need for population level interventions to smoking cessation and tobacco control in the developing countries. Health information systems (HIS) have made a significant role over the years in both the developed and developing countries. In order to provide a comprehensive health risk assessment profile for populations in the developing countries, however, there is an urgent need to develop epidemiological surveillance data to be integrated within HIS. This is principally important for health surveillance data on structure, process, risk factors, and outcome of cancer, given the economic, clinical and public health burden of cancer particularly in low- and middle-income developing countries. A comprehensive approach to cancer prevention and control in the developing countries should involve systematic and timely epidemiological surveillance. Such surveillance systems should be established keeping in mind the unique socio-economic, environmental, and cultural influences on cancer incidence in the developing countries.
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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 | 0.121 | 0.212 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.013 | 0.009 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.012 | 0.019 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.006 | 0.010 |
| 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".