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Record W2765827748 · doi:10.1177/1087054717738084

Psychometric Properties of the Persian Version of the Weiss Functional Impairment Rating Scale–Self-Report Form in Iranian Adolescents

2017· article· en· W2765827748 on OpenAlexaff
Habib Hadianfard, Behnaz Kiani, Margaret D. Weiss

Bibliographic record

VenueJournal of Attention Disorders · 2017
Typearticle
Languageen
FieldMedicine
TopicAttention Deficit Hyperactivity Disorder
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsCronbach's alphaPsychologyClinical psychologyRating scaleReliability (semiconductor)PsychometricsPersianScale (ratio)Convergent validityInternal consistencyFunctional impairmentQuality of life (healthcare)Developmental psychologyPsychotherapist

Abstract

fetched live from OpenAlex

OBJECTIVE: The Weiss Functional Impairment Rating Scale-Self-Report Form (WFIRS-S) was developed as a measure of functional impairment in adolescents and adults with ADHD. This study reports the psychometric properties of the Persian version of the WFIRS-S in a sample of normal Iranian adolescents. METHOD: Internal consistency and factor structure of the WFIRS-S were tested on a sample of 386 students (Grades 7-12). The test-retest reliability and the convergent validity of the WFIRS-S were evaluated by using two different subsamples including 50 and 100 students, respectively. RESULTS: The Cronbach's alpha values were between .72 and .94 for the WFIRS-S subdomains and total scale. The test-retest reliability was .80 for the total scale. The WFIRS-S subdomains had moderate to high significant correlations with the Pediatric Quality of Life total scale. CONCLUSION: The Persian version of the WFIRS-S has acceptable psychometric properties and could be used as a functional impairment assessment for adolescents.

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)0.001
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 score0.014
Threshold uncertainty score0.340

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.025
GPT teacher head0.280
Teacher spread0.255 · 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

Citations15
Published2017
Admission routes1
Has abstractyes

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