Development and Cross-Cultural Adaptation of the Tamil Version of the Stroke-Specific Quality of Life Scale (SSQoL) and Assessment of its Reliability and Validity
Bibliographic record
Abstract
BACKGROUND: In this current modern industrial world, strokes are the major reason for causing disability and death in the adult population. In spite of the various tools available to measure the physical, psychological, and social impact of strokes, the appropriate method in various languages around the world is not available. In that sense adapting the Stroke-Specific Quality of Life Scale (SSQoL) in different languages and cultures is essential to ensure their validity and efficacy across diverse populations. AIM: This study aims to translate the original SSQoL English version into the Tamil language and assess the scale's reliability and validity among Tamil-speaking subjects with chronic stroke survivors. METHODS: A methodological framework was applied to translate and culturally adapt SSQoL, involving forward and backward translation, committee review, and testing. A total of 220 participants were recruited to assess demographic characteristics, validity, and reliability of the Tamil-translated SSQoL-T using measures such as internal consistency, test-retest reliability, and convergent validity. RESULTS: The content validity analysis of the translated Tamil version of SSQoL-T showed strong positive outputs for both total score and sub-score assessments. In test-retest reliability analysis, good reliability with Cronbach's alpha (≥0.9) was observed for both total score and sub-score assessments. Conclusion: This study's findings underscore the content validity and good reliability of SSQoL-T as a screening tool for assessing stroke among Tamil-speaking populations, providing valuable insights for clinicians and researchers in the assessment and management of strokes.
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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 | 0.015 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".