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Record W4391163671 · doi:10.1177/17446295241229364

“Everything has changed since COVID”: Ongoing challenges faced by Canadian adults with intellectual disabilities during waves 2 and 3 of the COVID-19 pandemic

2024· article· en· W4391163671 on OpenAlexafffundabout
Yousef Safar, Fatima Formuli, Tiziana Volpe, Laura St. John, Yona Lunsky

Bibliographic record

VenueJournal of Intellectual Disabilities · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicElder Abuse and Neglect
Canadian institutionsUniversity of TorontoCentre for Addiction and Mental Health
FundersCanadian Institutes of Health Research
KeywordsPandemicThematic analysisCoronavirus disease 2019 (COVID-19)Intellectual disabilityAnticipation (artificial intelligence)PsychologyMental healthMedical educationQualitative researchPublic relationsMedicinePolitical scienceSociologyPsychiatrySocial science

Abstract

fetched live from OpenAlex

The COVID-19 pandemic has disrupted the lives of people with intellectual disabilities in many ways, impacting their health and wellbeing. Early in the pandemic, the research team delivered a six-week virtual group-based program to help Canadian adults with intellectual disabilities cope and better manage their mental health. The study's objective was to explore ongoing concerns among individuals with intellectual disabilities following their participation in this education and support program. Thematic analysis was used to analyze participant feedback provided eight weeks after course completion. Twenty-four participants were interviewed in January 2021 and May 2021 across two cycles of the course. Three themes emerged: 1) employment and financial challenges; 2) navigating changes and ongoing restrictions; and 3) vaccine anticipation and experience. These findings suggest that despite benefiting from the program, participants continued to experience pandemic-related challenges in 2021, emphasising the need to continually engage 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)0.001
metaresearch head score (Gemma)0.028
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.420
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.028
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.003
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.073
GPT teacher head0.298
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

Citations3
Published2024
Admission routes3
Has abstractyes

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