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Socioeconomic status and access to mental health care: The case of psychiatric medications for children in Ontario Canada

2023· article· en· W4389627518 on OpenAlexafffundabout
Janet Currie, Paul Kurdyak, Jonathan Zhang

Bibliographic record

VenueJournal of Health Economics · 2023
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHealthcare Policy and Management
Canadian institutionsMcMaster UniversityUniversity of Toronto
FundersSocial Sciences and Humanities Research Council of CanadaNOMIS StiftungInstitute for Clinical Evaluative Sciences
KeywordsSocioeconomic statusPsychiatryMedicineContext (archaeology)Mental healthDepression (economics)AnxietyPublic healthEnvironmental healthPopulation

Abstract

fetched live from OpenAlex

We examine differences in the prescribing of psychiatric medications to lower-income and higher-income children in the Canadian province of Ontario using rich administrative data that includes diagnosis codes and physician identifiers. Our most striking finding is that conditional on diagnosis and medical history, low-income children are more likely to be prescribed antipsychotics and benzodiazepines than higher-income children who see the same doctors. These are drugs with potentially dangerous side effects that ideally should be prescribed to children only under narrowly proscribed circumstances. Lower-income children are also less likely to be prescribed SSRIs, the first-line treatment for depression and anxiety conditional on diagnosis. Hence, socioeconomic differences in the prescribing of psychotropic medications to children persist even in the context of universal public health insurance and universal drug coverage.

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)0.002
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 score0.162
Threshold uncertainty score0.710

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.043
GPT teacher head0.324
Teacher spread0.282 · 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

Citations13
Published2023
Admission routes3
Has abstractyes

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