Bibliographic record
Abstract
Television has developed dramatically over the past six years in Afghanistan with a potential for providing extensive health information to its viewers, yet little is known about the coverage of health issues on Afghan television. Using the theories of agenda-setting and framing, and social norms approach this study explored what health-related topics were covered, how they were covered, and what sociocultural practices were broadcasted by the major Afghan private, national televisions. The study used a sample of six constructed weeks in 2010 of two leading private, national television networks. Firstly, the study found that priority health problems such as maternal and child health, communicable disease and mental health received very less coverage. Secondly, however, individual-level and societal-level causes were blamed equally for the health problems; individual behaviour solutions were the favourite choice of the media, turning a blind eye to government weakness and organizational solutions. Thirdly, self-prescription, religious and traditional health seeking behaviour, and gender inequity were routine practices reflected on television. As the first content analyses of the coverage of health-related issues in Afghanistan, the study provides public health professions, the Afghan media and policy makers a broad picture of health information available to the public on the leading Afghan television stations.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".