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Record W4400003049 · doi:10.1080/10400435.2024.2363383

Translation and validation of the Persian version of “The Psychosocial Impact of Assistive Devices Scale” in patients with neurological disorders

2024· article· en· W4400003049 on OpenAlexaff
Seyedeh Sareh Saeed, Mahnaz Hejazi‐Shirmard, Alireza Akbarzadeh Baghban, Jefferey Jutai, Mehdi Rezaee

Bibliographic record

VenueAssistive Technology · 2024
Typearticle
Languageen
FieldHealth Professions
TopicAssistive Technology in Communication and Mobility
Canadian institutionsUniversity of Ottawa
FundersShahid Beheshti University of Medical Sciences
KeywordsPsychosocialPersianScale (ratio)PsychologyPhysical medicine and rehabilitationApplied psychologyMedicinePhysical therapyEngineeringComputer sciencePsychiatry

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.012
Threshold uncertainty score0.461

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.022
GPT teacher head0.367
Teacher spread0.345 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2024
Admission routes1
Has abstractyes

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