Brief report on a systematic review of youth violence prevention through media campaigns: Does the limited yield of strong evidence imply methodological challenges or absence of effect?
Bibliographic record
Abstract
BACKGROUND: We present a brief report on a systematic review which identified, assessed and synthesized the existing evidence of the effectiveness of media campaigns in reducing youth violence. METHODS: Search strategies made use of terms for youth, violence and a range of terms relating to the intervention. An array of academic databases and websites were searched. RESULTS: Although media campaigns to reduce violence are widespread, only six studies met the inclusion criteria. There is little strong evidence to support a direct link between media campaigns and a reduction in youth violence. Several studies measure proxies for violence such as empathy or opinions related to violence, but the link between these measures and violence perpetration is unclear. Nonetheless, some evidence suggests that a targeted and context-specific campaign, especially when combined with other measures, can reduce violence. However, such campaigns are less cost-effective to replicate over large populations than generalised campaigns. CONCLUSIONS: It is unclear whether the paucity of evidence represents a null effect or methodological challenges with evaluating media campaigns. Future studies need to be carefully planned to accommodate for methodological difficulties as well as to identify the specific elements of campaigns that work, especially in lower and middle income 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 | 0.034 | 0.146 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.010 | 0.010 |
| Bibliometrics | 0.023 | 0.017 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 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".