Measuring compassion in end-of-life cancer patients: The Italian validation of the Sinclair Compassion Questionnaire (SCQit)
Bibliographic record
Abstract
OBJECTIVES: Compassion is acknowledged as a key component of high-quality palliative care, producing positive outcomes for both patients and healthcare providers. The development of the Sinclair Compassion Questionnaire (SCQ) fulfilled the need for a valid and reliable tool to measure patients' experience of compassion. To validate the Italian version of the SCQ and to evaluate its psychometric properties in a sample of cancer patients with a life expectancy of less than 4 months. METHODS: Cronbach's alpha estimates were computed to evaluate the internal reliability. Exploratory Factor Analysis, Confirmatory Factor Analysis, and Item Response Theory analyses were performed to assess the validity of the construct. Divergent validity was assessed using the Functional Assessment of Chronic Illness Therapy-Treatment Satisfaction-Patient Satisfaction, the revised Edmonton Symptom Assessment Scale, and the Trust in Oncologist Scale-Short Form. Data were collected from 131 patients recruited in either a hospital or a hospice setting. RESULTS: The analyses confirmed the single factor structure of SCQit, with Confirmatory Factor Analysis factor loadings ranging between 0.81 and 0.92 and satisfactory internal reliability. Hospital setting and high diagnosis/prognosis awareness were associated with significantly lower SCQit scores, whereas practicing a religious faith was associated with greater experiences of compassion. SIGNIFICANCE OF RESULTS: The Italian version of the SCQ (SCit) is a valid and reliable measure of patient-reported compassion. The SCQit can be used in clinical practice and research to measure the compassion experiences of terminally ill cancer patients and to evaluate the effectiveness of training to promote compassionate care in healthcare professionals.
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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 | 0.004 | 0.012 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".