Evaluating the psychosocial impacts of e-care use among older people receiving long-term care in Slovenia through the adapted PIADS-10 scale
Bibliographic record
Abstract
PURPOSE: Assistive technologies (ATs), including e-care, are increasingly vital in supporting the ageing population's long-term care; however, there is a scarcity of studies analysing the psychosocial impacts of e-care use among older people. The goal of this paper is twofold: first, to understand the psychosocial impacts of e-care use among older people receiving long-term care, and second, to evaluate the measurement properties of an internationally recognised self-report questionnaire, the Psychosocial Impact of Assistive Devices Scale (PIADS-10), specifically its multidimensionality and internal consistency of identified subscales. MATERIALS AND METHODS: A one-group post-test-only quasi-experimental intervention design was employed. Psychosocial impacts of e-care use were examined through an intervention study involving 217 older people in Slovenia, who tested e-care over an average period of 310.2 days. Participants were selected using purposive sampling. RESULTS: Findings revealed important enhancements in perceived security, sense of control, independence and overall quality of life among e-care users. Exploratory factor analysis of the PIADS-10 yielded two factors (psychological well-being and adaptability), supporting a reduced 8-item version (SI-PIADS-8), which was further supported through confirmatory factor analysis. CONCLUSIONS: These findings indicate that the PIADS-10 could be refined to an 8-item, two-factor version with improved relevance for assessing psychosocial impacts among older adults using e-care. Such insights can help stakeholders design and adapt ATs that better support ageing in place.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".