Cancer prevention campaigns and Internet access: promoting health or disease?
Bibliographic record
Abstract
BACKGROUND: The Internet has become the most popular source of reference on health-related issues. However, little has been invested in studies that use it as a tool to evaluate health-related cultural events as cancer prevention campaigns. AIMS: To correlate audience patterns on the web pages of a Brazilian Ministry of Health (MOH) cancer institute (http://www.inca.gov.br) with government campaigns in this area. METHODS: 24 consecutive months of observational study of a cancer site sponsored by the MOH, using a commercial software package to analyse electronic records (log files) of all visitors' movements. Variables observed included number of visits, time spent on each visit and the monthly return rate at six selected pages (three relating to cancer as a disease and three to prevention measures). RESULTS: The audience was observed to grow gradually over the 2 years, with peaks in the periods around the campaigns. The topics of most interest were concentrated in pages on cancer diagnostic and treatment technology. Pages on preventive measures were less visited during the campaigns, and their audience varied little over the 24 months. CONCLUSION: A historical analysis of log files for reference sites revealed interesting patterns that may be helpful for planning and evaluating institutional campaigns. PRACTICAL IMPLICATIONS: in view of the results of this study, the website was improved to offer better information on preferred topics and to include more links with prevention-related pages. Log file assessment after health campaigns could provide useful input to planning.
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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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".