Risk and protective factors associated with adolescent depression in Singapore: a systematic review
Bibliographic record
Abstract
INTRODUCTION: Adolescent depression is prevalent, and teen suicide rates are on the rise locally. A systemic review to understand associated risk and protective factors is important to strengthen measures for the prevention and early detection of adolescent depression and suicide in Singapore. This systematic review aims to identify the factors associated with adolescent depression in Singapore. METHODS: A systematic search on the following databases was performed on 21 May 2020: PubMed, EMBASE and PsycINFO. Full texts were reviewed for eligibility, and the included studies were appraised for quality using the Newcastle Ottawa Scale. Narrative synthesis of the finalised articles was performed through thematic analysis. RESULTS: In total, eight studies were included in this review. The four factors associated with adolescent depression identified were: (1) sociodemographic factors (gender, ethnicity); (2) psychological factors, including childhood maltreatment exposure and psychological constructs (hope, optimism); (3) coexisting chronic medical conditions (asthma); and (4) lifestyle factors (sleep inadequacy, excessive internet use and pathological gaming). CONCLUSION: The identified factors were largely similar to those reported in the global literature, except for sleep inadequacy along with conspicuously absent factors such as academic stress and strict parenting, which should prompt further research in these areas. Further research should focus on current and prospective interventions to improve mental health literacy, targeting sleep duration, internet use and gaming, and mitigating the risk of depression in patients with chronic disease in the primary care and community setting.
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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 | 0.005 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".