Child and adolescent self-harm in a pandemic world: Evidence from a decade of data
Bibliographic record
Abstract
BACKGROUND: Little is known about the COVID-19 pandemic impact on child and adolescent mental health, specifically self-harm. This paper serves to form a basis for understanding and planning an appropriate response to the present and longstanding child and adolescent mental health needs with global recommendations for integrated community support and disaster preparedness. METHODS: Anonymous, aggregated data from an established regional child and adolescent addictions and mental health service was employed to examine differences in the rates of self-harm as the primary reason for referral among the health-seeking population represented by quarter by year from 2010-2022 to examine whether self-harm rates have increased since the onset of the COVID-19 pandemic. RESULTS: Females and self-defined sex had higher rates of self-harm referrals compared to males. Both total and first-time self-harm referrals since the COVID outbreak in 2020 did not exceed the highest quarterly rates before COVID since 2010. DISCUSSION: Since the COVID-19 pandemic, self-harm rates in one Canadian region remain stable and lower than the highest rates previously observed over the last decade. Given misplaced alarmist news and reports, a coherent, evidence-based, dynamic national response to mental health, social support, and disaster planning is required to fully understand how best to respond to the pandemic in general with a sustainable social support and disaster preparedness policy strategy and specifically the ongoing and pandemic-related mental health needs of the child and adolescent help-seeking population.
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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 | 0.036 | 0.124 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".