Determinants of the number of ATDs used by community-dwelling people recovering from stroke with mild cognitive impairments: A path analysis
Bibliographic record
Abstract
Objective: This study examined the associative relationships among age, cognition, anxiety, and participation in explaining the number of Assistive Technology Devices (ATDs) used by stroke survivors through path analysis. Methods: A cross-sectional study was conducted with 196 community-dwelling stroke survivors. Data on ATD usage, cognitive function (Montreal Cognitive Assessment), anxiety (Beck Anxiety Inventory), and participation (Utrecht Scale for Evaluation of Rehabilitation-Participation) were analyzed using SPSS 22.0 and Amos 24.0. Results: The modified path model demonstrated a good fit to the data. Age, anxiety, and participation had direct effects on the number of ATDs used, while cognition did not show a statistically significant effect. Anxiety also had an indirect effect through participation, indicating a dual role of participation in either increasing or reducing ATD reliance. Age influenced anxiety and participation both directly and indirectly. Conclusions: This study identified the pathways through which age, anxiety, and participation influence ATD usage among stroke survivors. Given the complexity of interplay of psychological and functional factors, ATD prescriptions should adopt a user-centered approach, considering participation levels, psychological responses, and environmental factors to optimize effectiveness and long-term use.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".