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Record W7139327021

An Examination of Mental Health Service Utilization Measurement and Associations with Social Determinants of Health in Youth

2025· dissertation· W7139327021 on OpenAlexaff
Yukiko Mihashi

Bibliographic record

VenueTSpace (University of Toronto) · 2025
Typedissertation
Language
FieldPsychology
TopicMental Health Treatment and Access
Canadian institutionsInstitute for Work & Health
Fundersnot available
KeywordsMental healthMental health serviceService (business)Social determinants of healthCohortData collectionService provider
DOInot available

Abstract

fetched live from OpenAlex

Despite the burden of mental health challenges in youth, only 25% receive necessary care. There is currently no gold standard measure to assess mental health service utilization in youth, and findings are mixed on how social determinants of health (SDOH) influence service use patterns. This thesis presents a scoping review of standalone instruments with evaluation of measurement properties used to assess mental health service use in youth, and a secondary analysis of service use and SDOH data from an ongoing prospective cohort study. The scoping review identified N=8 instruments with varying degrees of measurement property evaluation, instances of use, and services assessed. The secondary data analysis found higher rates of outpatient service use in those engaged in education, employment, or training and hospitalization among those assigned female at birth. These results add to the growing body of literature aiming to address gaps in youth mental health service use.

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)0.015
metaresearch head score (Gemma)0.039
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 score0.081
Threshold uncertainty score0.161

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0150.039
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0060.011
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0000.001
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 head0.089
GPT teacher head0.378
Teacher spread0.289 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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