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Record W2763912175 · doi:10.1093/pch/pxx086.014

THE ROLE OF ONTARIO’S FIRST SCHOOL-BASED HEALTH CLINIC IN ACCESSING DEVELOPMENTAL AND MENTAL HEALTH CARE FOR CHILDREN FROM INNER CITY ELEMENTARY SCHOOLS: A RETROSPECTIVE CHART REVIEW

2017· article· en· W2763912175 on OpenAlexaffabout
Amy Cheung, Thivia Jegathesan, Anne Mantini, J. H. H. Chan, R Vijendra Das, Ramanan Aiyadurai, Steve Freeman

Bibliographic record

VenuePaediatrics & Child Health · 2017
Typearticle
Languageen
FieldHealth Professions
TopicChild and Adolescent Health
Canadian institutionsSt. Michael's HospitalToronto Metropolitan UniversityWomen's College HospitalUniversity of Toronto
Fundersnot available
KeywordsMental healthMedicineHealth careFamily medicinePediatricsGerontologyPsychiatry

Abstract

fetched live from OpenAlex

BACKGROUND: Children with developmental and mental health challenges face an uphill battle to reach the academic milestones of their peers. These challenges are exacerbated by social inequity. Early access to developmental and mental health assessments has been shown to improve long-term outcomes. Unfortunately, the wait-time for developmental assessment in Ontario is 15.5 months and the wait-time for mental health assessment in Ontario has not been well documented. The first School-Based Health Clinic (SBHC) in Ontario was established through the Model Schools Pediatric Health Initiative as a partnership between St. Michael’s Hospital and the Toronto District School Board. The clinic was modeled after SBHCs in the United States which provide comprehensive medical and mental health care to underserved children. A feasibility study conducted in 2012 during the first eight months of the program demonstrated 20% of SBHC users went on to receive a developmental assessment. The current proportion of users receiving a developmental or mental health assessment at the SBHC and wait-times to assessment has not been explored. OBJECTIVES: To assess the impact of a School-Based Health Clinic on wait-times for developmental assessments and mental health care for inner-city children. DESIGN/METHODS: A retrospective chart review was performed on 404 children aged 4–14 with at least one visit to an inner-city School-Based Health Clinic from 2011–2014. Developmental impact measures included the proportion of children presenting with a developmental concern and the wait-time for receiving a developmental assessment. Mental health impact measures included the proportion of children with mental health concerns, proportion of children with new mental health diagnoses, proportion of children receiving a referral to a mental health professional, and wait-times for receiving a mental health diagnosis. RESULTS: Of the 404 children with at least one visit to the School-Based Health Clinic, 71.3% had a developmental concern and the wait-time for receiving a developmental assessment was 27.6 weeks. Mean age of children receiving a developmental assessment was 7.0 years. With respect to mental health impact, 55.9% presented to the school clinic with at least one mental health concern, 13.6% received a new mental health diagnosis including at least one of attention deficit hyperactivity disorder, oppositional defiant disorder, conduct disorder, depression, anxiety disorder, or selective mutism, and 25.7% of the children received a referral to a psychiatrist or psychologist. The wait-time for receiving a mental health diagnosis was 27.1 weeks. CONCLUSION: School-Based Health Clinics are an effective and feasible model to decrease wait-times for receiving developmental assessments and to provide mental health services to inner-city children.

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)0.003
metaresearch head score (Gemma)0.008
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 score0.324
Threshold uncertainty score0.651

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.008
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0090.011
Science and technology studies0.0020.001
Scholarly communication0.0020.001
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.030
GPT teacher head0.373
Teacher spread0.343 · 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

Citations5
Published2017
Admission routes2
Has abstractyes

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