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 since 2010 to examine whether self-harm rates have increased since the onset of the COVID-19 pandemic. RESULTS Female rates of self-harm referral were greater than male rates. Neither male nor female quarterly rates of total or first-time self-harm referrals exceeded the highest quarterly rates since 2010. DISCUSSION Since the COVID-19 pandemic, self-harm rates in one Canadian region remain stable and lower than the highest rates 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 | 0.029 | 0.093 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.010 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".