Prevalence of Common Child Mental Health Disorders Using Administrative Health Data and Parent Report in a Prospective Community-Based Cohort from Alberta, Canada: Prévalence des troubles communs de santé mentale de l’enfant à l’aide des données de santé administratives et des rapports des parents dans une cohorte prospective communautaire d’Alberta, Canada
Bibliographic record
Abstract
OBJECTIVE: Knowing the prevalence of mental health difficulties in young children is critical for early identification and intervention. In the current study, we examine the agreement among three different data sources estimating the prevalence of diagnoses for attention deficit hyperactivity disorder (ADHD) and emotional disorders (i.e., anxiety or mood disorder) for children between birth and 9 years of age. METHODS: Data from a prospective pregnancy cohort was linked with provincial administrative health data for children in Alberta, Canada. We report the positive agreement, negative agreement, and Cohen's Kappa of parent-reported child diagnoses provided by a health professional ("parent report"), exceeding a clinical cut-off on a standardized questionnaire completed by parents (the Behavior Assessment System for Children, 3rd edition ["BASC-3"]), and cumulative inpatient, outpatient, or physician claims diagnoses ("administrative data"). RESULTS: Positive and negative agreement for administrative data and parent-reported ADHD diagnoses were 70.8% and 95.6%, respectively, and 30.5% and 94.9% for administrative data and the BASC-3, respectively. For emotional disorders, administrative data and parent-reported diagnoses had a positive agreement of 35.7% and negative agreement of 96.30%. Positive and negative agreement for emotional disorders using administrative data and the BASC-3 were 20.0% and 87.4%, respectively. Kappa coefficients were generally low, indicating poor chance-corrected agreement between these data sources. CONCLUSIONS: The data sources highlighted in this study provide disparate agreement for the prevalence of ADHD and emotional disorder diagnoses in young children. Low Kappa coefficients suggest that parent-reported diagnoses, clinically elevated symptoms using a standardized questionnaire, and diagnoses from administrative data serve different purposes and provide discrete estimates of mental health difficulties in early childhood. PLAIN LANGUAGE TITLE: Prevalence of child mental health disorders according to different data sources in Canada.
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".