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Record W2145810116 · doi:10.1080/19315864.2013.798389

A Population-Based Longitudinal Study of Depression in Children With Developmental Disabilities in Manitoba

2014· article· en· W2145810116 on OpenAlexaffabout
Shahin Shooshtari, Marni Brownell, Natalia Dik, Dan Château, C. T. Yu, Rosemary S. L. Mills, Charles Burchill, Monika Wetzel

Bibliographic record

VenueJournal of Mental Health Research in Intellectual Disabilities · 2014
Typearticle
Languageen
FieldPsychology
TopicFamily and Disability Support Research
Canadian institutionsManitoba HealthUniversity of Manitoba
Fundersnot available
KeywordsDepression (economics)CohortDemographyResidencePopulationMental healthIntellectual disabilityMedicineCohort studyPsychologyPediatricsGerontologyPsychiatryEnvironmental health

Abstract

fetched live from OpenAlex

AbstractIn this population-based study, prevalence of depression was estimated and compared between children with and without developmental disability (DD). Twelve years of administrative data were linked to identify a cohort of children with DD living in the Canadian province of Manitoba. Children in the study cohort were matched with children without DD as to sex, age, and region of residence. Prevalence of depression was estimated and compared between the two groups using the Generalized Estimating Equations technique. It was found that the estimated prevalence of depression among children with DD was almost twice as high as that of children in the matched comparison group. The estimated relative risk was statistically significant, RR = 2.13 (95% CI: 1.94, 2.33, p < .001). With age, the prevalence of depression among children in both groups increased. These findings suggest an urgent need for the development of mental health promotion programs targeted at children with DD.KEYWORDS: intellectual/developmental disabilitieschildrenadolescentsdepressionadministrative datarecord linkage

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)0.008
metaresearch head score (Gemma)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.311
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.212
GPT teacher head0.455
Teacher spread0.243 · 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.

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
Published2014
Admission routes2
Has abstractyes

Explore more

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