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Record W3122090262 · doi:10.1111/jar.12856

Health and service use of newcomers and other adults with intellectual and developmental disabilities: A population‐based study

2021· article· en· W3122090262 on OpenAlexaffabout
Anna Durbin, James K. H. Jung, Hannah Chung, Elizabeth Lin, Robert Balogh, Yona Lunsky

Bibliographic record

VenueJournal of Applied Research in Intellectual Disabilities · 2021
Typearticle
Languageen
FieldMedicine
TopicDown syndrome and intellectual disability research
Canadian institutionsCentre for Addiction and Mental HealthPublic Health OntarioUniversity of TorontoOntario Tech UniversitySt. Michael's Hospital
Fundersnot available
KeywordsIntellectual disabilityPopulationGerontologyMultiple disabilitiesPsychologyMedicinePsychiatryDevelopmental psychologyEnvironmental health

Abstract

fetched live from OpenAlex

BACKGROUND: This study examines newcomers with intellectual and developmental disabilities compared to other adults with intellectual and developmental disabilities in Ontario, Canada. METHODS: This population-based retrospective cohort study used linked health and social services administrative data to identify adults with intellectual and developmental disabilities as newcomers, or non-newcomers, and compared their health status and health service outcomes. RESULTS: Among those with intellectual and developmental disabilities, compared to non-newcomers, newcomers generally had lower or similar rates of health issues, except for higher rates of psychosis. Newcomers also had slightly greater use of community-based health services, but less hospital use. CONCLUSION: Trends among those with the intellectual and developmental disabilities were consistent with general population trends; newcomers had lower rates of many health issues and lower hospital use. It also underscores the value of understanding drivers of heterogeneity within newcomers, such as the circumstances of admission and settlement in their new country.

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)0.023
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.396
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.023
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.165
GPT teacher head0.390
Teacher spread0.225 · 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 designQualitative
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

Citations1
Published2021
Admission routes2
Has abstractyes

Explore more

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