Personalized Citizen Assistance for Social Participation (APIC) adapted for older adults with visual impairment: results from a mixed study
Bibliographic record
Abstract
PURPOSE: To explore the effects of the Personalized Citizen Assistance for Social Participation (APIC), an intervention adapted here for visual impairment, involving weekly stimulation sessions over six to twelve months, provided by trained and supervised attendants, on seven outcomes (social participation, leisure, independence, mobility, quality of life, health-related quality of life, and empowerment) in older adults with visual impairment, and to document its facilitators and barriers. METHODS: A mixed-method design, which included a pre-experimental and an exploratory qualitative clinical research component, was used on 8 older adults (7 women) with visual impairment aged 70-86, and 8 attendants (5 women) aged 20-74. Before the intervention, directly after, and four months later, older adults completed questionnaires on the 7 outcomes. During the intervention, attendants completed diaries and participated in monthly meetings. Semi-structured interviews were administered to all participants after the intervention. RESULTS: Social participation, leisure, mobility, quality of life and empowerment had increased immediately after the APIC. These improvements were still generally observed four months later. Participants reported that the APIC improved older adults' capabilities, social participation, and social environment. CONCLUSIONS: The APIC is a promising intervention which helps older adults with visual impairment to deal with social restrictions.
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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 | 0.015 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".