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Record W3032251092 · doi:10.1177/1087054720930816

Reliability, Criterion and Concurrent Validity of the Farsi Translation of DIVA-5: A Semi-Structured Diagnostic Interview for Adults With ADHD

2020· article· en· W3032251092 on OpenAlexaff
Lida Zamani, Zahra Shahrivar, Javad Alaghband‐Rad, Vandad Sharifi‎, Elham Davoodi, Shadi Ansari, Fatemeh Emari, Dora Wynchank, J. J. Sandra Kooij, Philip Asherson

Bibliographic record

VenueJournal of Attention Disorders · 2020
Typearticle
Languageen
FieldMedicine
TopicAttention Deficit Hyperactivity Disorder
Canadian institutionsMcGill University
FundersTehran University of Medical Sciences and Health Services
KeywordsDivaPsychologyMedical diagnosisRating scaleConstruct validityClinical psychologyCriterion validityAttention deficit hyperactivity disorderPsychometricsReliability (semiconductor)PopulationPsychiatryMedicineDevelopmental psychologyPathology

Abstract

fetched live from OpenAlex

Objectives: This study evaluated the psychometrics of the Farsi translation of diagnostic interview for attention-deficit hyperactivity disorder (ADHD) in adults (DIVA-5) based on DSM-5 criteria. Methods: Referrals to a psychiatric outpatient clinic ( N = 120, 61.7% males, mean age 34.35 ± 9.84 years) presenting for an adult ADHD (AADHD) diagnosis, were evaluated using the structured clinical interviews for DSM-5 (SCID-5 & SCID-5-PD) and the DIVA-5. The participants completed Conner’s Adult ADHD Rating Scale-Self Report-Screening Version (CAARS-S-SV). Results: According to the SCID-5 and DIVA-5 diagnoses, 55% and 38% of the participants had ADHD, respectively. Diagnostic agreement was 81.66% between DIVA-5/SCID-5 diagnoses, 80% between SCID-5/CAARS-S-SV, and 71.66% between DIVA-5/CAARS-S-SV. Test–retest and inter-rater reliability results for the DIVA-5 were good to excellent. Conclusion: Findings support the validity and reliability of the Farsi translation of DIVA-5 among the Farsi-speaking adult outpatient population.

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)0.001
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 score0.210
Threshold uncertainty score0.360

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.059
GPT teacher head0.320
Teacher spread0.261 · 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

Citations21
Published2020
Admission routes1
Has abstractyes

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