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Record W2804912186 · doi:10.1093/pch/pxy054.015

ATTENTION-DEFICIT/HYPERACTIVITY DISORDER (ADHD) IN SCHOOL-AGED CHILDREN AT TWO SCHOOL BASED HEALTH CENTERS (SBHCS): A DESCRIPTIVE STUDY

2018· article· en· W2804912186 on OpenAlexaffabout
Nayantara Ghosh, Ramanan Aiyadurai, Sloane Freeman

Bibliographic record

VenuePaediatrics & Child Health · 2018
Typearticle
Languageen
FieldMedicine
TopicAttention Deficit Hyperactivity Disorder
Canadian institutionsSt. Michael's HospitalUniversity of Toronto
Fundersnot available
KeywordsAttention deficit hyperactivity disorderMedicinePovertyPopulationPediatricsEthnic groupAttention deficitPsychiatryEnvironmental health

Abstract

fetched live from OpenAlex

Abstract BACKGROUND The number of school-aged children diagnosed with ADHD in Canada has been on the rise over the past three decades. Evidence suggests that children with ADHD dealing with risk factors, such as poverty and prolonged wait-times are more likely to have poorer outcomes due to challenges in accessing healthcare services. Schools are ideal for the early identification of children with ADHD, as they are often the setting in which attention and behavioural issues come to light. School-Based Health Centres (SBHCs) are embedded within the school system and are an ideal entry point for children with ADHD into the healthcare system. OBJECTIVES To examine the prevalence of ADHD and as well as demographic characteristics and time to assessment of children at two inner-city SBHCs in Toronto. DESIGN/METHODS A retrospective chart review was performed on 869 children from November 2010- March 2016 from two SBHCs. Frequency measures were used to determine the proportion of children that received a new diagnosis of ADHD. Within this population, the patient’s age, gender, ethnicity, parental income, home arrangement, parental education and newcomer status were described. Diagnostic wait-times within the SBHC were calculated using two specific data points – a child’s first clinic visit data and the clinic date they saw a general paediatrician, who would provide the ADHD diagnosis. RESULTS Of the 869 children, 9.6% of children received a new diagnosis of ADHD. The mean age of diagnosis was 7.6 years and 80% of the children were male. 74.6% of children’s families identified them as an ethnicity other than white. 60.2% of the patients’ household income was <$30,000/year. 44.5% of the families were composed of single-parent households. 52.8% of the patients’ mothers and 47.6% of fathers had completed a high school level of education or less. 34% of the children were not born in Canada, and of those, 57% had been in the country for only 0–3 years. The average wait time for a child to see a general paediatrician for a developmental assessment from initial visit date was 62.3 days. CONCLUSION The prevalence of ADHD at 2 SBHCs was higher than that reported in the general population. A number of barriers to health care access were identified in this cohort of children including low income, single parent homes and newcomer status. SBHCs serve as an accessible health care model that can provide timely diagnosis and management to vulnerable children with ADHD which may improve outcomes.

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)0.002
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 score0.379
Threshold uncertainty score0.753

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.006
Science and technology studies0.0020.001
Scholarly communication0.0010.001
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 head0.028
GPT teacher head0.329
Teacher spread0.301 · 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
Published2018
Admission routes2
Has abstractyes

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