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Record W4408810260 · doi:10.1111/jar.70037

Understanding Barriers to Mental Health Supports During the Pandemic for Workers in Intellectual Disability Services

2025· article· en· W4408810260 on OpenAlexafffundabout
Madelaine Carter, Nicole Bobbette, Sabrina Campanella, Yona Lunsky

Bibliographic record

VenueJournal of Applied Research in Intellectual Disabilities · 2025
Typearticle
Languageen
FieldMedicine
TopicDown syndrome and intellectual disability research
Canadian institutionsQueen's UniversityPublic Health OntarioUniversity of TorontoCentre for Addiction and Mental Health
FundersAzrieli Foundation
KeywordsWorkforceMental healthIntellectual disabilityMental distressDistressPandemicPsychologyWorkforce developmentWork (physics)MedicinePsychiatryNursingCoronavirus disease 2019 (COVID-19)Clinical psychologyPolitical science

Abstract

fetched live from OpenAlex

BACKGROUND: Workers in the intellectual disability sector encounter significant work-related stress impacting their mental health. This study explores the barriers faced by these workers when accessing mental health support during the pandemic. METHODS: 1831 surveys were completed by intellectual disability support workers from Ontario, Canada between 2021 and 2023. This is a secondary analysis of questions focused on barriers to accessing mental health services. RESULTS: On average, 45% of workers reported experiencing barriers to accessing mental health support each year. The cost of services and lack of time were consistently identified as barriers. Workers reporting barriers were more likely to be younger, have less than 10 years of sector-specific experience, and report significant mental distress. CONCLUSION: Sector-wide efforts to reduce barriers and improve the mental health of workers are critical to promote the wellbeing of the workforce and to support high-quality care for people with intellectual disabilities.

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)0.010
metaresearch head score (Gemma)0.026
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), 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 score0.456
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0100.026
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.003
Science and technology studies0.0010.002
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.002
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 head0.158
GPT teacher head0.427
Teacher spread0.269 · 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
Published2025
Admission routes3
Has abstractyes

Explore more

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