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Record W270313903 · doi:10.1177/070674370805300305

Trends in Psychostimulant and Antidepressant Use by Children in 2 Canadian Provinces

2008· article· en· W270313903 on OpenAlexaffvenueabout
Brendon Mitchell, Bruce Carleton, Anne Smith, Robert J. Prosser, Marni Brownell, Anita L. Kozyrskyj

Bibliographic record

VenueThe Canadian Journal of Psychiatry · 2008
Typearticle
Languageen
FieldPsychology
TopicChild and Adolescent Psychosocial and Emotional Development
Canadian institutionsChildren's & Women's Health Centre of British ColumbiaManitoba HealthUniversity of British ColumbiaUniversity of Manitoba
Fundersnot available
KeywordsMedical prescriptionAntidepressantMedicineDemographyPsychiatry

Abstract

fetched live from OpenAlex

OBJECTIVE: We used population-based administrative prescription medication data to examine regional differences in psychostimulant and antidepressant (AD) use among children from 2 Canadian provinces: British Columbia (BC) and Manitoba (MB). METHOD: Using 1997 to 2003 prescription data, annual rates of psychostimulant and AD use were determined for children aged 19 years and under in both provinces. Further comparisons of rates were made according to sex, age group, and specific classes of dispensed medications. RESULTS: During 1997 to 2003, psychostimulant use rose by 44.9% in MB and 13.3% in BC. Among male children, psychostimulant use increased by 40.2% in MB, compared with an increase of only 8.6% in BC. AD utilization was similar between provinces, with increases of 80% and 75% in MB and BC, respectively. In both provinces, AD use was highest among older children. CONCLUSIONS: Our observations of regional variation in psychotropic medication use potentially reflect provincial differences in drug benefit policies, disease prevalence, and (or) physician diagnosis and treatment.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.033
Threshold uncertainty score0.240

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0030.005
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.015
GPT teacher head0.249
Teacher spread0.234 · 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 source (direct Gemma or distilled Codex), 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

Citations19
Published2008
Admission routes3
Has abstractyes

Explore more

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