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Record W3007284421 · doi:10.1111/jar.12698

Healthcare services utilization among people with intellectual disability and comparison with the general population

2020· article· en· W3007284421 on OpenAlexafffundabout
Julie Maltais, Diane Morin, Marc J. Tassé

Bibliographic record

VenueJournal of Applied Research in Intellectual Disabilities · 2020
Typearticle
Languageen
FieldMedicine
TopicDown syndrome and intellectual disability research
Canadian institutionsUniversité du Québec à Montréal
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsIntellectual disabilityHealth carePopulationHealth servicesPsychologyNursingPsychiatryMedicineGerontologyEnvironmental healthEconomic growthEconomics

Abstract

fetched live from OpenAlex

BACKGROUND: Studies have reported unmet health needs in individuals with intellectual disability (ID). This study illustrated and analysed patterns of healthcare services utilization among people with intellectual disability and compared their use to that of the general population. METHOD: Participants (N = 791, aged 15-82) were mainly recruited through government-financed agencies specializing in services for people with intellectual disabilities in Québec, Canada. Comparisons were possible by using health administrative data. RESULTS: Some services were more used by people with intellectual disability than the general population (general medicine, psychiatry, PSA blood tests), and others were accessed at significantly lower frequencies (optometry, physiotherapy, Pap tests). Similar rates were found for mammography, dentistry and psychology. Inequities were more salient for individuals who had more severe levels of intellectual disability. CONCLUSIONS: Our findings support that the population with intellectual disability would benefit from policies and practices aimed at enhancing the access to healthcare services.

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)0.001
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 score0.225
Threshold uncertainty score0.447

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.001
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 head0.134
GPT teacher head0.394
Teacher spread0.259 · 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

Citations42
Published2020
Admission routes3
Has abstractyes

Explore more

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