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Record W4387195264 · doi:10.1177/27550834231200617

Accessing and re-accessing mental health walk-in clinics for children and families

2023· article· en· W4387195264 on OpenAlexafffundabout
Catalina Sarmiento, Graham J. Reid

Bibliographic record

VenueThe Journal of Medicine Access · 2023
Typearticle
Languageen
FieldPsychology
TopicChild and Adolescent Psychosocial and Emotional Development
Canadian institutionsChildren’s Health Research InstituteWestern University
FundersChildren’s Health FoundationCanadian Institutes of Health ResearchChildren's Health FoundationChildren's Health Research Institute
KeywordsLegal guardianMental healthDemographicsAgency (philosophy)MedicineDescriptive statisticsFamily medicinePsychiatryDemography

Abstract

fetched live from OpenAlex

Background: Many child and youth mental health (CYMH) agencies across Canada and in Ontario are using mental health walk-in clinics (MHWCs). Objectives: (1) Explore how MHWCs are used by families (e.g. mean, mode, and median number of visits), and (2) document how often and how soon families returned for a second MHWC visit and identify correlates of time to a second MHWC visit. Design: Administrative data from two CYMH agencies in Ontario were extracted, including demographics, visit data, and presenting concerns. Methods: In this exploratory, descriptive study, analyses of administrative data were conducted to identify patterns and correlates of MHWC use before other agency services, compared to MHWC use exclusively. Results: About a third of children and families using MHWCs had two or more visits. Child age, guardianship, and disposition at discharge emerged as correlates of time to a second MHWC visit. Conclusion: MHWCs can save families' time, and both agencies' time and money by eliminating the need to complete a detailed assessment prior to treatment for cases that would go on to have a single visit within this service.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.719
Threshold uncertainty score0.258

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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 head0.067
GPT teacher head0.423
Teacher spread0.356 · 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

Citations0
Published2023
Admission routes3
Has abstractyes

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