McGill Quality of Life Questionnaire: Reliability and Validity of the Persian Version in Iranian Patients with Advanced Cancer
Bibliographic record
Abstract
OBJECTIVES: McGill Quality of Life Questionnaire (MQOL) is an instrument specifically designed to evaluate quality of life (QOL) in patients with advanced diseases. The primary objective of this study was to translate this questionnaire into Persian and assess its reliability and validity in Iranian patients suffering from an advanced cancer. The report also includes the correlations between patients' characteristics and their QOL scores. METHODS: MQOL was translated into Persian using a forward-backward method and administered to 62 patients with cancer at two hospitals in Tehran. Statistical analysis was performed to test reliability and validity of the questionnaire and to assess relationships between patients' characteristics and MQOL scores. RESULTS: In general, MQOL-Persian is a valid and reliable questionnaire to assess QOL in Iranian patients with advanced cancer. Test-retest reliability of the whole questionnaire is highly favorable for a questionnaire of this type (Pearson correlation = 0.87). Except for the existential subscale, all MQOL scores are internally consistent. There was no significant correlation between MQOL scores and patients' extent of knowledge about their disease. CONCLUSION: The Persian version of MQOL is, for the most part, a valid, reliable instrument in this setting. Because of cultural differences, the existential subscale may require adaptation. Further studies are needed to explore other aspects of QOL in Iranian patients with cancer.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".