Managing population health to prevent and detect cancer and non-communicable diseases.
Bibliographic record
Abstract
The goals of cancer control strategies are generally uniform across all constituencies and are to reduce cancer incidence, reduce cancer mortality, and improve quality of life for those affected by cancer. A well-constructed strategy will ensure that all of its elements can ultimately be connected to one of these goals. When a cancer control strategy is being implemented, it is essential to map progress towards these goals; without mapping progress, it is impossible to assess which components of the strategy require more attention or resources and which are not having the desired effect and need to be re-evaluated. In order to monitor and evaluate these strategies, systems need to be put in place to collect data and the appropriate indicators of performance need to be identified. Session 2 of the 4th International Cancer Control Congress (ICCC-4) focused on how to manage population health to prevent and detect cancers and non-communicable diseases through two plenary presentations and four interactive workshop discussions: 1) registries, measurement, and management in cancer control; 2) use of information for planning and evaluating screening and early detection programs; 3) alternative models for promoting community health, integrated care and illness management; and 4) control of non-communicable diseases. Workshop discussions highlighted that population based cancer registries are fundamental to understanding the cancer burden within a country. However, many countries in Africa, Asia, and South/ Central America do not have them in place. A new global initiative is underway, which brings together several international agencies, and aims to establish six IARC regional registration resource centres over the next five years. These will provide training, support, infrastructure and advocacy to local networks of cancer registries, and, it is hoped, improve the host countries' ability to assess and act on cancer issues within their jurisdictions. Multiple methods of programme evaluation were presented across workshops, but all were attuned to both the resource base and the specific questions to be addressed. Where innovative strategies were being tested, customized evaluation strategies should be undertaken. Where programmes are well-developed and data is being collected for evaluation, there is the opportunity for sophisticated analytical methods to be used to pinpoint specific areas or delivery sites for future quality improvement. Finally, unique opportunities now exist to integrate the strategies developed in cancer control and evaluation with those under development for other non-communicable diseases. This area will likely be one for future development.
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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 | 0.035 | 0.018 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".