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Record W4212915287 · doi:10.1177/15394492221078315

Social Networks May Shape Visually Impaired Older Adults’ Occupational Engagement: A Narrative Inquiry

2022· article· en· W4212915287 on OpenAlexaff
Ji Won Kang, Colleen McGrath, Debbie Laliberté Rudman, Carri Hand

Bibliographic record

VenueOTJR Occupational Therapy Journal of Research · 2022
Typearticle
Languageen
FieldMedicine
TopicOphthalmology and Visual Impairment Studies
Canadian institutionsWestern UniversityUniversity of Waterloo
Fundersnot available
KeywordsPsychosocialThematic analysisReciprocity (cultural anthropology)Social engagementPsychologyNarrativeSocial supportOccupational therapyNarrative inquiryAdaptation (eye)Developmental psychologyGerontologyApplied psychologySocial psychologyQualitative researchSociologyMedicinePsychotherapist

Abstract

fetched live from OpenAlex

Age-related vision loss (ARVL) has been shown to interfere with older adults' occupational engagement. The primary purpose was to examine the role social networks play in facilitating/constraining engagement in desired occupations for older adults with ARVL. This study adopted a constructivist narrative methodology. Five older adults, ≥ 60 years of age with ARVL, participated in three virtual interviews, which were coded using thematic analysis. Three overarching themes were identified: (a) Diverse Social Networks Fulfill Different Occupational and Psychosocial Needs, (b) Retaining a Sense of Independence through Seeking Reciprocity in Social Relationships, and (c) Community Mobility and Technology Support as Essential for Preserving Social Relationships. Findings broaden understandings of how informal/formal social networks are involved in shaping visually-impaired older adults' adaptation to ARVL and related occupational changes. Findings may help improve the quality and delivery of low-vision rehabilitation services to optimize their contribution to occupational engagement.

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)0.005
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.230
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0060.000

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 head0.258
GPT teacher head0.526
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 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

Citations1
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueOTJR Occupational Therapy Journal of ResearchSame topicOphthalmology and Visual Impairment StudiesFrench-language works237,207