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Record W2990119186 · doi:10.1136/jech-2018-211698

Mental health of parents of children with a developmental disability in British Columbia, Canada

2019· article· en· W2990119186 on OpenAlexafffundabout
Sandra Marquis, Kimberlyn McGrail, Michael Hayes

Bibliographic record

VenueJournal of Epidemiology & Community Health · 2019
Typearticle
Languageen
FieldPsychology
TopicFamily and Disability Support Research
Canadian institutionsUniversity of British ColumbiaUniversity of Victoria
FundersUniversity of Victoria
KeywordsMental healthPopulationOddsDepression (economics)Christian ministryPsychologyMedicineIntellectual disabilityPsychiatryLogistic regressionEnvironmental health

Abstract

fetched live from OpenAlex

BACKGROUND: There is evidence in the literature that parents of children who have a developmental disability experience an increased risk of mental health problems. METHODS: This study used population-level administrative data from the Ministry of Health, British Columbia, Canada, to assess the mental health of parents of children who have a developmental disability compared with the mental health of parents of children who do not have a developmental disability. Population-level and individual explanatory variables available in the data were included in the models. RESULTS: At a population level, the study found strong evidence that parents of children who have a developmental disability experience higher odds of depression or other mental health diagnoses compared with parents of children who do not have a developmental disability. Age of the parent at birth of the child, income and location of healthcare services were all associated with outcomes. CONCLUSION: Parents of children who have a developmental disability may be in need of programmes 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 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 score0.015
Threshold uncertainty score0.112

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0020.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.001
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 head0.065
GPT teacher head0.389
Teacher spread0.324 · 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

Citations64
Published2019
Admission routes3
Has abstractyes

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