Analyses of Child and Youth Self-Poisoning Hospitalizations by Substance and Socioeconomic Status
Bibliographic record
Abstract
Child and youth self-poisoning is a growing public health issue in many regions of the world, including British Columbia (BC), Canada, where 15-19-year-olds have the highest rates of self-poisoning hospitalizations compared with those of all other ages. The purpose of this study was to identify what substances children and youth commonly used to poison themselves in BC and how socioeconomic status may impact self-poisoning risk. Self-poisoning hospitalization rates among 10-14 and 15-19-year-olds from 1 April 2012 to 31 March 2020 were calculated by substance using ICD-10-CA codes X60-X69 and T36-T65, as well as by socioeconomic status using the Institut National de Santé Publique du Québec's Deprivation Index. Nonopioid analgesics, antipyretics, and antirheumatics were the most common substances involved, with rates of 27.6 and 74.3 per 100,000 population among 10-14 and 15-19-year-olds, respectively, followed by antiepileptic, sedative-hypnotic, antiparkinsonism, and psychotropic drugs, with rates of 20.2 and 68.1 per 100,000 population among 10-14 and 15-19-year-olds, respectively. In terms of socioeconomic status, rates were highest among 10-19-year-olds living in neighbourhoods with the fewest social connections (243.7 per 100,000 population). These findings can inform poisoning prevention strategies and relevant policies, thereby reducing the number of self-poisoning events among children and youth.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".