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Record W4393924803 · doi:10.1177/17446295241245784

Examining sedentary behaviours of adults with intellectual disabilities: A qualitative analysis

2024· article· en· W4393924803 on OpenAlexafffundabout
Sana Safi, Gina Wong, Lorraine M. Thirsk, Jeff K. Vallance

Bibliographic record

VenueJournal of Intellectual Disabilities · 2024
Typearticle
Languageen
FieldMedicine
TopicDown syndrome and intellectual disability research
Canadian institutionsAthabasca University
FundersAdministration for Community LivingCanada Research ChairsAthabasca University
KeywordsIntellectual disabilityPsychologySedentary lifestyleQualitative researchPopulationGerontologyPhysical activityMedicineEnvironmental healthPsychiatryPhysical therapy

Abstract

fetched live from OpenAlex

Sedentary behaviours among adults with intellectual disabilities have not been well studied. A sedentary lifestyle puts adults with and without disabilities at high risk of developing health conditions and diseases. Current literature revealed few empirical studies on the benefits of reducing sedentary behaviours with respect to the health of adults with intellectual disabilities. This research explored the factors that helped or hindered sedentary behaviours of adults with intellectual disabilities in the Canadian population. Guided by the socio-ecological model, Critical Incident Technique (CIT) was conducted. Five adults with intellectual disabilities from the Province of Ontario were interviewed and 102 critical incidents were collected. Adults with intellectual disabilities identified personal and environmental related factors that led to increased sedentary behaviours; and revealed helpful factors and wish-lists of actions that decreased sedentary lifestyle. Findings may be useful when developing programs aimed to decrease prolonged periods of sedentary behaviours specific to this vulnerable population.

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)0.033
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Science and technology studies, 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 score0.031
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.033
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0020.003
Science and technology studies0.0000.003
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0220.000

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 head0.078
GPT teacher head0.386
Teacher spread0.308 · 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
Published2024
Admission routes3
Has abstractyes

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