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Record W4411540594 · doi:10.1080/02703181.2025.2521639

Daily Activities and Social Participation Among Occupational Therapists Working with Older Adults with Neurocognitive Disorders: A Canadian-Wide Survey

2025· article· en· W4411540594 on OpenAlexaffabout
Alia Osman, Patrícia Belchior, Isabelle Gélinas

Bibliographic record

VenuePhysical & Occupational Therapy In Geriatrics · 2025
Typearticle
Languageen
FieldMedicine
TopicCerebral Palsy and Movement Disorders
Canadian institutionsInstitut Universitaire de Gériatrie de MontréalMcGill UniversityCentre for Interdisciplinary Research in Rehabilitation
Fundersnot available
KeywordsNeurocognitiveOccupational therapyPsychologyGerontologyClinical psychologyMedicinePsychiatryCognition

Abstract

fetched live from OpenAlex

Aim Activities of daily living (ADL) and social participation are key predictors of health outcomes in older adults with Alzheimer’s disease (AD) or related disorders. This study aimed to profile occupational therapy (OT) practice in using standardized ADL and social participation questionnaires among older adults with AD or related disorders across Canada.Methods A cross-sectional survey was conducted nationwide among OTs working with older adults with AD. Participants were recruited through OT regulatory bodies and the Canadian Association of Occupational Therapists.Results A total of 183 OTs responded, with 41.53% assessing ADLs and 6.01% assessing social participation using standardized questionnaires. Barriers included the belief that these tools are unnecessary, lack of access, and time constraints. Most OTs relied on colleagues to select assessment methods.Conclusion Future research is required to improve accessibility to and practitioners’ knowledge of standardized ADL and social participation questionnaires.

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)0.002
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 score0.021
Threshold uncertainty score0.073

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.004
Science and technology studies0.0020.000
Scholarly communication0.0010.000
Open science0.0010.001
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 head0.026
GPT teacher head0.302
Teacher spread0.275 · 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

Citations0
Published2025
Admission routes2
Has abstractyes

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