Development and validation of a patient-reported measure of compassion in healthcare: the Sinclair Compassion Questionnaire (SCQ)
Bibliographic record
Abstract
OBJECTIVES: Compassion is a key indicator of quality care that is reportedly eroding from patients' care experience. While the need to assess compassion is recognised, valid and reliable measures are lacking. This study developed and validated a clinically informed, psychometrically rigorous, patient-reported compassion measure. DESIGN: Data were collected from participants living with life-limiting illnesses over two study phases across four care settings (acute care, hospice, long term care (LTC) and homecare). In phase 1, data were analysed through exploratory factor analysis (EFA), with the final items analysed via confirmatory factor analysis (CFA) in phase 2. The Schwartz Center Compassionate Care Scale (SCCCS), the revised Edmonton Symptom Assessment Scale (ESAS-r) and Picker Patient Experience Questionnaire (PPEQ) were also administered in phase 2 to assess convergent and divergent validity. SETTING AND PARTICIPANTS: 633 participants were recruited over two study phases. In the EFA phase, a 54-item version of the measure was administered to 303 participants, with 330 participants being administered the final 15-item measure in the CFA phase. RESULTS: Both EFA and CFA confirmed compassion as a single factor construct with factor loadings for the 15-item measure ranging from 0.76 to 0.86, with excellent test-retest reliability (intraclass correlation coefficient range: 0.74-0.89) and excellent internal reliability (Cronbach's alpha of 0.96). The measure was positively correlated with the SCCCS (r=0.75, p<0.001) and PPEQ (r=0.60, p<0.001). Participants reporting higher experiences of compassion had significantly greater well-being and lower depression on the ESAS-r. Patients in acute care and hospice reported significantly greater experiences of compassion than LTC residents. CONCLUSIONS: There is strong initial psychometric evidence for the Sinclair Compassion Questionnaire (SCQ) as a valid and reliable patient-reported compassion measure. The SCQ provides healthcare providers, settings and administrators the means to routinely measure patients experiences of compassion, while providing researchers a robust measure to conduct high-quality research.
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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 | 0.013 | 0.019 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 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".