Hospitalizations due to self-poisoning at a Canadian paediatric hospital
Bibliographic record
Abstract
BACKGROUND: Adolescent self-harm by drug ingestion (i.e., self-poisoning) is a serious mental health issue. In Newfoundland and Labrador (NL), paediatricians suspected an increase in the number of adolescents hospitalized due to self-poisoning in the province. Our primary aim was to evaluate the number of hospital admissions of adolescents for self-poisoning between 2008 and 2013 to determine whether there was indeed an increase in hospitalizations. We also wanted to examine the characteristics of these admissions to better understand this patient population. METHOD: A retrospective chart review was conducted to identify cases of self-poisoning admitted to the only paediatric hospital in NL over a 6-year period. A data abstraction form was developed to collect patient demographic information and details about these incidences of self-poisoning. RESULTS: A total of 156 patient admissions were identified; 97 (62.2%) first time admissions and 59 (37.8%) recurrent admissions. The number of admissions for self-poisoning increased over the study period from 2.1% of total hospital admissions in 2008 to 6.5% in 2013. Mean (SD) age at the time of admission was 15.4 years, 122 patients (78.2%) were female and 86.5% had at least 1 previous mental health diagnosis. The most common drugs ingested were analgesics (38.0%) and antidepressants (32.2%), with 73 patients (48.7%) ingesting multiple drugs. CONCLUSIONS: The study contributes to the growing recognition of adolescent self-poisoning as a serious paediatric mental health issue. It also confirmed that an increase in adolescent hospitalizations due to self-poisoning has occurred in NL. Further research is warranted to identify effective prevention strategies for this serious problem.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".