Translation and validation of the Persian version of “The Psychosocial Impact of Assistive Devices Scale” in patients with neurological disorders
Bibliographic record
Abstract
Neurological disorders are a heterogeneous group of central or peripheral nervous disorders of which the main symptoms include impaired walking and balance. One of the main interventions for neurological disorders is the use of assistive devices, and it is necessary to consider the psychosocial effects of these devices on users. The psychometric properties of the Persian version of the Psychosocial Impact of Assistive Devices Scale (PIADS) were evaluated in patients with neurological disorders. After translating the scale into Persian based on IQULA, face and content validity were determined. The divergent validity of the scale was examined through its relationship with the Orthotics and Prosthetics Users' Survey (OPUS). Reliability of the tool was evaluated using an internal consistency and test-retest method over two weeks with 50 patients with neurological disorders and a history of using assistive devices for at least six months. The face and content validity of the PIADS was confirmed. The ICC for all subscales was higher than 0.78, which indicates a good correlation. However, the divergent validity of the scale with the OPUS scale was not confirmed. The Persian version of PIADS is a valid and reliable measure for patients with neurological disorders in Iran.
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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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".