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Record W4414060760 · doi:10.1080/17489539.2025.2543946

The Impact Scale for Assessment of Cluttering and Stuttering (ISACS): preliminary analyses

2024· article· en· W4414060760 on OpenAlex
Pallavi Kelkar, Maya Sanghi, Sneha Chaudhari

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueEvidence-Based Communication Assessment and Intervention · 2024
Typearticle
Languageen
FieldPsychology
TopicStuttering Research and Treatment
Canadian institutionsCalgary Laboratory Services
Fundersnot available
KeywordsStutteringScale (ratio)PsychometricsPreference

Abstract

fetched live from OpenAlex

The International Classification of Functioning, Disability and Health stresses the contribution of the environment to the overall impact of a disorder. However, current standardized tools for stuttering assessment do not assess impact from the perspective of significant others. There is specifically a dearth of impact assessment tools tailored to the Indian sociocultural milieu. The Impact Scale for Assessment of Cluttering and Stuttering (ISACS) was constructed to fill this gap in assessment. It was translated to Marathi and tested for equivalence to the original English version to increase the feasibility of data collection in rural and urban areas of Maharashtra. Fifty-two persons with fluency disorders (PWF), their significant others, and 52 typical speakers responded to the ISACS. Psychometric evaluation revealed good reliability, construct validity, and face validity. The ISACS is the first tool, to the authors’ knowledge, that assesses the overall impact of stuttering and cluttering from two perspectives. Future directions include adding to the data pool, factor analysis, and translation to other Indian languages to widen the scope of its utility across India.

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.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.796
Threshold uncertainty score0.429

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.222
GPT teacher head0.567
Teacher spread0.346 · 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