Are media literacy interventions effective at changing attitudes and intentions towards risky health behaviors in adolescents? A meta‐analytic review
Bibliographic record
Abstract
Youth are inundated with media products promoting risky health behaviors (RHBs), including substance use and risky sexual activity. Media literacy interventions emphasize critical media consumption to decrease RHBs. However, it is unclear whether they positively influence attitudes and behavioral intentions towards RHBs. We conducted meta-analyses of 15 studies (N = 5000) testing intervention effectiveness on media literacy skills and 20 studies (N = 9177) testing effectiveness on attitudes and intentions towards RHBs. We found positive effects on media literacy skills (Hedge's g = .417, [95% CI, .29-.54]) and attitudes and intentions (Hedge's g = .100 [95% CI, .01-.19]). Intervention medium and target behavior moderated intervention success on attitudes and intentions, but no moderators emerged for media literacy skills. These interventions produce positive effects on media literacy skills and positive but smaller effects on attitudes and behavioral intentions, depending on medium and target behaviour. Implications for adolescent health initiatives are discussed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.011 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 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".