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Record W4366778124 · doi:10.1080/01924788.2023.2204578

Feasibility of Increasing Social Participation for Older Adults with Disabilities

2023· article· en· W4366778124 on OpenAlexafffund
Mélanie Levasseur, Hélène Lefebvre, Marie‐Josée Levert, Joanie Lacasse‐Bédard, Julie Lacerte, Hélène Carbonneau, Pierre-Yves Thérriault

Bibliographic record

VenueActivities Adaptation & Aging · 2023
Typearticle
Languageen
FieldHealth Professions
TopicAging, Elder Care, and Social Issues
Canadian institutionsHealth and Social Services Centre University Institute of Geriatrics of SherbrookeUniversité de MontréalCentre for Interdisciplinary Research in RehabilitationUniversité du Québec à Trois-RivièresCentre Intégré Universitaire de Santé et de Services Sociaux du Centre-Sud-de-l'Île-de-MontréalUniversité de Sherbrooke
FundersFonds de Recherche du Québec - SantéCanadian Institutes of Health Research
KeywordsPsychologyAcronymQualitative researchSocial mediaMedical educationGerontologyApplied psychologySociologyMedicinePolitical scienceSocial science

Abstract

fetched live from OpenAlex

This study aimed to adapt the personalized citizen assistance for social participation (APIC; French acronym for Accompagnement-citoyen personnalisé d’intégration communautaire) approach for older adults with disabilities and explore its feasibility. A qualitative clinical research design, including interviews, was used with 19 older adults with disabilities. They met with their nonprofessional attendant (citizen supervised by the research team) about 20 times and 16 of them completed the APIC. Personal facilitators and barriers to physical, intellectual, manual, artistic and social interaction experiences were related to health and sensory, motor or behavioral capabilities, including psychological and emotional availability. Results also highlighted environmental facilitators (e.g., paratransit, social support) and barriers (e.g. inaccessibility, weather, over-protective family). The APIC represents new opportunities for older adults to achieve community integration and enhance their social life and resources, while confirming the feasibility of addressing the global “aging well” priority.

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)0.012
metaresearch head score (Gemma)0.026
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.012
Threshold uncertainty score0.061

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0120.026
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.000
Science and technology studies0.0020.001
Scholarly communication0.0020.001
Open science0.0010.005
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.108
GPT teacher head0.420
Teacher spread0.312 · 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

Citations10
Published2023
Admission routes2
Has abstractyes

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