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Record W2955814957 · doi:10.1016/j.ssmph.2019.100441

A population-level study of the mental health of siblings of children who have a developmental disability

2019· article· en· W2955814957 on OpenAlexaffabout
Sandra Marquis, Kimberlyn McGrail, Michael V. Hayes

Bibliographic record

VenueSSM - Population Health · 2019
Typearticle
Languageen
FieldPsychology
TopicFamily and Disability Support Research
Canadian institutionsUniversity of British ColumbiaUniversity of Victoria
Fundersnot available
KeywordsMental healthDepression (economics)PopulationPsychiatryMedicineStressorPsychologyEnvironmental health

Abstract

fetched live from OpenAlex

This study used population level administrative data for health service utilization from the Ministry of Health, British Columbia, Canada to assess the mental health of siblings of children who have a developmental disability. At a population level, the study found strong evidence that siblings of children who have a developmental disability experience higher odds of a depression or other mental health diagnosis compared to siblings of children who do not have a developmental disability. In addition, there was evidence that in families with a child with a developmental disability, siblings who are diagnosed with depression or another mental health problem use physician and/or hospital services for these conditions to a greater extent than siblings who are diagnosed with depression or a mental health problem but do not have a family member with a developmental disability. Evidence of increased depression and mental health problems existed across all income levels, indicating that other stressors may have an impact. These findings suggest that siblings of children who have a developmental disability are a vulnerable group in need of programs and services that support their mental health.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.098
Threshold uncertainty score0.903

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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)0.0010.000

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 head0.071
GPT teacher head0.410
Teacher spread0.339 · 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

Citations32
Published2019
Admission routes2
Has abstractyes

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