Italian validation of the McGill Quality of Life Questionnaire (MQOL-It).
Bibliographic record
Abstract
INTRODUCTION: Quality of life is a dynamic concept that can undergo change with time and/or disease progression. The aim of this study was to test the reliability and validity of the Italian version of the McGill Quality of Life Questionnaire (MQOL-It), that we consider useful for assessing quality of life in Palliative Care. METHODS: The MQOL-It was administered by interview to 175 patients (M 108; F 67) admitted to a Unit of Palliative Care. All patients were suffering from advanced disease: cancer, amyotrophic lateral sclerosis, chronic heart failure. Statistical analysis was performed to assess the psychometric properties of the questionnaire. RESULTS: Factor analysis (VARIMAX) revealed four domains of quality of life, though the item composition differed, at composition analysis, from the original MQOL version. "Achieved goals" and "control over life", items classified in the English version as part of the existential domain, in the Italian version fitted the psychological domain; the item "well-being" was grouped into the physical domain and did not load clearly with other factors. Cronbach's alpha for the whole questionnaire was 0.85, with a good internal consistency for the four subscales (Cronbach's alpha > or = 0.65). All MQOL-It subscales were significantly correlated (Spearman correlation) with the Single Item rating Scale (SIS); comparison between the MQOL-It and the Nottingham Health Profile (NHP)-part I showed the instrument's concurrent validity. CONCLUSIONS: MQOL shows robust psychometric properties and appears suitable for evaluating quality of life in palliative care in Italy.
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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 | 0.008 | 0.020 |
| 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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".