The Validation of the Sinclair Compassion Questionnaire (SCQ) and SCQ Short Form in an English-Speaking U.S. Population: A Patient-Reported Measure of Compassion in Healthcare
Bibliographic record
Abstract
Background: Compassion is recognized as a key component of high-quality healthcare. The literature shows that compassion is essential to improving patient-reported outcomes and fostering health care professionals’ (HCPs) response and resilience to burnout. However, compassion is inherently difficult to define, and a validated tool to reliably quantify and measure patients’ experience of compassion in healthcare settings did not exist until recently. The Sinclair Compassion Questionnaire (SCQ) was compared to six similar tools in 2022 and emerged as the most reliable tool to assess compassion. The purpose of our study was to validate the SCQ in an English-speaking U.S. population. Methods: A total of 272 patients completed our survey, which included the SCQ and 17 demographic-related questions. A confirmatory factor analysis (CFA) was conducted to establish the construct validity of the SCQ and also the five-item version, the SCQ Short Form (SCQ-SF). Results: The CFA confirmed a good model fit, with factor loadings ranging from 0.81 to 0.93. Further analysis showed strong reliability, ranging from 0.866 to 0.957, and with an overall Cronbach’s alpha = 0.96. Conclusions: This study validates the SCQ and SCQ-SF in an English-speaking U.S. population and provides researchers and HCPs with a reliable psychometric tool to measure compassion across healthcare settings.
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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 | 0.018 |
| 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 | 0.001 | 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".