Cross-cultural adaptation and validation of the Arabic version of McGill quality of life: revised questionnaire in the patients with cancer
Bibliographic record
Abstract
PURPOSE: The aims of this study were to translate and culturally adapt the McGill Quality of Life Questionnaire-revised (MQOL-R) to modern standard Arabic and to examine its reliability, construct, and discriminative validity in Arab patients with cancer. MATERIALS AND METHODS: Translation and cultural adaptation of the English MQOL-R to modern standard Arabic were performed according to international guidelines. For psychometric evaluation, 125 participants with cancer were selected and completed the MQOL-R along with Global Health Status/QoL and functional subscales of European Organization for Research and Treatment of Cancer Quality of Life Questionnaire-Core 30 (EORTC QLQ-C30), and Eastern Cooperative Oncology Group performance status rating (ECOG-PS). The MQOL-R was tested for internal consistency, test-retest reliability, and construct validity. RESULTS: < 0.001). As hypothesized, the Arabic MQOL-R subscales demonstrated moderate to excellent correlation with functional subscales of EORTC QLQ-C30, and moderate to good correlation with Global health status/QoL. CONCLUSION: The Arabic MQOL-R Questionnaire has adequate psychometric properties. Hence, it can be utilized in rehabilitation settings and research to measure health-related quality of life in the Arabic-speaking cancer population.
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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 | 0.007 | 0.018 |
| 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 | 0.001 |
| 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".