Effectiveness of Mass and Small Media Campaigns to Improve Cancer Awareness and Screening Rates in Asia: A Systematic Review
Bibliographic record
Abstract
PURPOSE: The main objective of this systematic review was to identify whether mass and small media interventions improve knowledge and attitudes about cancer, cancer screening rates, and early detection of cancer in Asia. METHODS: The review was conducted according to a predefined protocol. Medline, EMBASE, CINAHL, Web of Science, Cochrane Library, and Google Scholar were searched in September 2017, and data extraction and rating of methodologic study quality (according to Joanna Briggs Institute rating procedures) were performed independently by reviewers. RESULTS: Twenty-two studies (reported across 24 papers) met the inclusion criteria. Most studies (n = 21) were conducted in high or upper-middle income countries; targeted breast (n = 11), cervical (n = 7), colorectal (n = 3), or oral (n = 2) cancer; and used small media either alone (n = 15) or in combination with mass media and other components (n = 5). Studies regarding cancer screening uptake were of medium to high quality and mainly reported positive outcomes for cervical cancer and mixed results for breast and colorectal cancer. The methodologic strength of research that investigated change in cancer-related knowledge and the cost effectiveness of interventions, respectively, were weak and inconclusive. CONCLUSION: Evidence indicated that small media campaigns seemed to be effective in terms of increasing screening uptake in Asia, in particular cervical cancer screening. Because of the limited number of studies in Asia, it was not possible to be certain about the effectiveness of mass media in improving screening uptake and the effectiveness of campaigns in improving cancer-related knowledge.
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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 | 0.011 | 0.045 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.009 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".