The Italian McGill Quality of Life Questionnaire-Revised (MQoL-R): Psychometrics in Neurological and Neoplastic Populations
Bibliographic record
Abstract
Background The McGill Quality of Life Questionnaire-Revised (MQoL-R) is the gold standard for assessing QoL in end-of-life, chronic patients; however, an Italian standardization is lacking. Objective This study aimed at assessing the psychometric properties of the Italian MQoL-R in patients with chronic neurological/oncological conditions. Methods 177 inpatients with life-threatening, chronic neurological/oncological conditions were consecutively recruited in 8 clinics in Northern/Southern Italy were administered the MQoL-R and the Karnofsky Performance Status (KPS). Factorial structure (Confirmatory Factor Analysis, CFA), reliability (Cronbach's α) and construct validity against the KPS (Pearson's coefficients) were examined. Results The four-factor model (Physical, Psychological, Existential and Social subscales) was met (comparative fit index = .93; root mean square error of approximation = .07), with all items significantly loading on respective subscales. Internal consistency was good for both the whole scale (Cronbach's α = .83) and subscales ( range = .6-.85). The KPS was unrelated to MQoL-R measures, except for the Physical subscale ( r = .24). Conclusions The Italian MQoL-R is a valid and reliable tool to assess QoL in end-of-life, both neoplastic and neurological, chronic inpatients undergoing palliative care, whose adoption is thus encouraged in both clinical practice and research addressed to such populations.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".