Stuttering As a Disability in Pakistan; Policies and Its Implementation
Bibliographic record
Abstract
Objective: Stuttering is a disorder of speech fluency not only includes academic and social consequences but also has emotional/behavioral outcomes. Stuttering leads to negative emotions and feelings i.e., low confidence, frustration, shame, embarrassment, poor self-image, and negative social identity. The present study was aimed at incorporating the definition of disability in Pakistan policy and determining the position of stuttering in it. Materials and Methods: The nature of the current study is qualitatively conducted on 10 Participants approached through convenient sapling and exploring their responses through Thematic analysis. Participants falling in this category were recruited into the study from SLPs, Lawyers, disability advocates, and CSPs as per inclusion criteria. Common masses were excluded from the study. A Self-Developed Questionnaire/structured interviews were conducted. Questions developed by the Lawshe method. The tool was validated through content validity by 10“experts”. Results: Thematic analysis was done and the findings of the study indicated an individual’s negative reaction towards association with any kind of disability and communication barriers are also the main source of not fitting disability as a definition. Results also showed that lack of assessment and screening tools are the main barrier, however, Visibility as per data in research publications also consider a barrier towards recognition of it. Conclusion: The study has practical implications regarding stuttering as a Disability in Pakistan.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
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".