The Impact Scale for Assessment of Cluttering and Stuttering (ISACS): preliminary analyses
Bibliographic record
Abstract
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.
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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".