A Systematic Review of the Impacts of Media Mental Health Awareness Campaigns on Young People
Bibliographic record
Abstract
Mental health issues are prevalent among young people. An estimated 10% of children and adolescents worldwide experience a mental disorder, yet most do not seek or receive care. Media mental health awareness campaigns, defined as marketing efforts to raise awareness of mental health issues through mass media, are an effort to address this concern. While previous research has evaluated the outcomes of specific media mental health awareness campaigns, there is limited data synthesizing their overall effects. This study addresses the knowledge gap by reviewing the existing literature on the impact of media mental health awareness campaigns on young people. A search was conducted on MEDLINE, EMBASE, PsychINFO, Web of Science, and Google Scholar for studies published between 2004 and 2022 with results specific to people aged 10 to 24. Out of 20,902 total studies identified and screened, 18 studies were included in the review. The following data were extracted from each study: characteristics and descriptions of the campaign, evaluation design and sampling, and summary of impact. The review identified evaluations of 15 campaigns from eight different countries. Outcome evaluation methods commonly comprised of surveys and quantitative data. The campaigns were generally associated with positive changes in the attitudes, beliefs, and intentions of young people (e.g., reduced stigma) and positive changes in behaviors (e.g., increased help-seeking behaviors). The inclusion of few studies in the review indicates a need for ongoing evaluations of media mental health awareness campaigns for young people to inform good practices in their development and distribution.
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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 | 0.009 | 0.041 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.005 |
| Bibliometrics | 0.015 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".