Occupational Engagement of Older Adults with Age-Related Vision Loss (ARVL): Understanding the Influences of Social Networks
Bibliographic record
Abstract
The present study sought to understand how older adults’ social networks facilitate and constrain their engagement in meaningful occupations after being diagnosed with age-related vision loss (ARVL). A constructivist paradigm and narrative inquiry methodology were used to elicit and make sense of the participants’ unique stories. The participants consisted of five older adults 60 years and older, living with ARVL, including one of the following conditions; macular degeneration, glaucoma, and diabetic retinopathy. Participants were recruited from the Canadian National Institute for the Blind (CNIB) and Society for Learning and Retirement (SLR). Data was collected through three sessions of semi-structured, audio-recorded virtual interviews (via Zoom and telephone calls). Participants were inquired about their experiences of interacting with social networks and engaging in desired occupations while living with ARVL. Thematic and structural narrative analyses (Riesman, 2008) were performed on participants’ stories which identified the following five dominant themes: (1) Maintaining Engagement in Social Occupations to Foster a Sense of Belonging; (2) Diverse Social Networks Fulfill Different Occupational and Psychosocial Needs; (3) Retaining a Sense of Independence through Seeking Reciprocity in Social Relationships; (4) Community Mobility as Essential for Preserving Social Relationships; and (5) Technology as a Support to Social Connectedness: Connecting via Technology versus in Person. This research expands knowledge on ARVL-related barriers and facilitators to occupational engagement and highlights the benefits of social support in maintaining visually impaired older adults’ occupational goals. The future directions and implications of the study findings on future research and vision care services are also discussed.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".