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 distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".