Quality of Life after Stroke: Evaluation of the Greek SAQOL-39g
Bibliographic record
Abstract
BACKGROUND/AIMS: Stroke and aphasia rehabilitation aims to improve people's quality of life. Yet, scales for measuring health-related quality of life in stroke typically exclude people with aphasia. They are also primarily available in English. An exception is the 39-item generic version of the Stroke and Aphasia Quality of Life Scale (SAQOL-39g). This scale has been tested with people with aphasia; it has been adapted for use in many countries including Greece. The aim of this study was to examine the psychometric properties of the Greek SAQOL-39g. METHODS: An interview-based psychometric study was carried out. Participants completed: receptive subtests of the Frenchay Aphasia Screening Test, the Greek SAQOL-39g, the 12-item General Health Questionnaire, the Frenchay Activities Index, the Montreal Cognitive Assessment and the Barthel Index. RESULTS: 86 people took part; 26 provided test-retest reliability data. The Greek SAQOL-39g demonstrated excellent acceptability (minimal missing data; no floor/ceiling effects), test-retest reliability [intraclass correlation coefficient = 0.96 (overall scale), 0.83-0.99 (domains)] and internal consistency [Cronbach's alpha = 0.96 (overall scale), 0.92-0.96 (domains)]. There was strong evidence for convergent [r = 0.53-0.80 (overall scale), 0.54-0.89 (domains)] and discriminant validity [r = 0.52 (overall scale), 0.04-0.48 (domains)]. CONCLUSION: The Greek SAQOL-39g is a valid and reliable scale. It is a promising measure for use in stroke and aphasia treatment prioritization, outcome measurement and service evaluation.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".