Age of onset of self-harm in children and adolescents: a scoping review
Bibliographic record
Abstract
BACKGROUND: Self-harm is associated with significant distress in children and adolescents. The objective of this scoping review was to map the age of onset of self-harm in people aged ≤ 18 years alongside the definitions, operationalisation, and research methods used to determine onset. METHOD: Following JBI guidance, this review included studies reporting the age of onset of self-harm in people aged ≤ 18 years in any context. Medline, PsycInfo, Embase, CINAHL Plus, and Web of Science were searched last on 7th May 2025 and supplemented by a grey literature search. Data were subject to basic coding and narrative and graphical presentation. RESULTS: A total of 42 studies were included in the review. Age of onset ranged from 9 to 18 years, but most studies reported a mean age between 12 and 14 years. The majority of studies defined self-harm as either suicidal or non-suicidal (85%), with non-suicidal self-harm distributed in favour of a slightly younger onset age. Studies with a younger sample tended to report a younger age of onset. Most studies used cross-sectional methods (81%) and retrospective report (71%) to capture onset age. CONCLUSIONS: Earlier age of onset is associated with the use of multiple methods of self-harm, self-harm of longer duration and increased frequency. A clear understanding of age of onset of self-harm is necessary to inform clinically relevant research and the timely targeting of developmentally prevention and early intervention strategies.
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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 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.011 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".