MétaCan
Menu
Back to cohort
Record W3174321934 · doi:10.3390/ijerph18137003

Analyses of Child and Youth Self-Poisoning Hospitalizations by Substance and Socioeconomic Status

2021· article· en· W3174321934 on OpenAlexaffabout
Samantha Pawer, Fahra Rajabali, Alex Zheng, Jennifer Smith, Roy Purssell, Ian Pike

Bibliographic record

VenueInternational Journal of Environmental Research and Public Health · 2021
Typearticle
Languageen
FieldMedicine
TopicPoisoning and overdose treatments
Canadian institutionsUniversity of British ColumbiaBC Centre for Disease ControlProvincial Health Services AuthorityBC Children's Hospital
Fundersnot available
KeywordsSocioeconomic statusMedicinePopulationDemographyInjury preventionPoison controlSuicide preventionPublic healthOccupational safety and healthEnvironmental healthGerontology

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.024
Threshold uncertainty score0.240

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.053
GPT teacher head0.392
Teacher spread0.339 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations29
Published2021
Admission routes2
Has abstractyes

Explore more

Same venueInternational Journal of Environmental Research and Public HealthSame topicPoisoning and overdose treatmentsFrench-language works237,207