A systematic review of depression literacy: Knowledge, help‐seeking and stigmatising attitudes among adolescents
Bibliographic record
Abstract
INTRODUCTION: Depression is a common mental health disorder and affects many adolescents worldwide. Depression literacy can improve mental health outcomes. The aim of this study was to collate and analyse the extant evidence on depression literacy among adolescents, with particular focus on tools used to examine depression literacy and the findings on components of depression literacy. METHODS: Nine electronic databases and 1 grey literature source were searched for studies published in English between January 2006 and December 2018 and involving adolescents aged 10-19 years. We included studies that reported on components of depression literacy such as knowledge, help-seeking and stigmatising attitudes. We excluded qualitative studies. Two independent reviewers verified that the studies met the inclusion criteria, assessed the quality of the studies and extracted their characteristics. The data were descriptively analysed and appraised using the Newcastle-Ottawa Scale (NOS), Cochrane Collaboration's tool and the Quality Assessment Tool for Quantitative Studies (QATSQ). RESULTS AND CONCLUSION: Fifty of the 14,626 references identified met the inclusion criteria. Depression literacy was most commonly (58%) assessed using tools that utilize a vignette-based methodology. A lack of uniformity in reporting of depression literacy was noted. Adolescents were poor at recognising depression, likely to seek help from informal sources and tended to attach stigma to depression. The implications of the findings are discussed and suggestions made for future research.
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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 | 0.007 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.005 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| 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".