Cross-cultural translation, adaptation, and validation of the stroke-specific quality of life (SSQOL) scale 2.0 into Amharic language
Bibliographic record
Abstract
BACKGROUND: The stroke-specific quality of life 2.0 (SSQOL 2.0) scale is a valid, reliable instrument which has been widely used as a patients reported outcome measure among stroke survivors. However, the SSQOL scale has not been validated and used in any Ethiopian language. This study aimed to translate, culturally adapt, and test the psychometric properties of the SSQOL scale 2.0 in Amharic, which is the official and working language with about 34 million (23%) speakers in Ethiopia. METHODS: The adapted English version of the SSQOL 2.0 scale was translated into Amharic and then back-translated to English. An expert committee translated and created a final Amharic version of SSQOL (SSQOL-AM) scale. Pre-field testing (pilot and cognitive debriefing) was conducted with 15 post-stroke subjects. The SSQOL-Am was administered to 245 stroke survivors from four referral hospitals to determine the psychometric properties. Cronbach's alpha and Intra-class correlation coefficient were used to calculate the internal consistency and test-retest reliability, spearman's correlation for the convergent validity of the SSQOL-Am scale. The Standard Error of Measurement (SEM), Minimum Detectable Change (MDC), Bland Altman Limit of Agreement (LOA), Confirmatory Factor Analysis, and Exploratory Factor Analysis were also determined. RESULTS: The SSQOL-Am demonstrated excellent test-retest reliability (ICC = 0.93), internal consistency (Cronbach's alpha = 0.96), SEM 0.857, MDC 1.94, and good LOA. As postulated, the mobility domain of the tool demonstrated a significantly strong correlation with the physical function domain of the SF-36 (rho = 0.70, p < 0.001). CONCLUSIONS: The SSQOL-Am is a valid and reliable outcome measure. The tool can be used in both clinical practice and research purposes with Amharic speaking post-stroke survivors.
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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 | 0.012 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".