Adaptation and validation of a patient‐reported compassion measure in the Spanish population: The Spanish version of the Sinclair Compassion Questionnaire (SCQesp)
Bibliographic record
Abstract
International practice guidelines and policies recognize compassion as a fundamental dimension of quality care. A key element in enhancing compassion in healthcare settings is having reliable patient-reported experience measures. In the Spanish context, there is a need for a valid Spanish patient-reported compassion measure for use in both research and clinical practice. The Sinclair Compassion Questionnaire (SCQ) represents the gold standard for patient-reported compassion measures in English-speaking settings. The primary aim of this study is to culturally adapt and validate the SCQ in a Spanish population. A Spanish version of the SCQ (SCQesp) was used to collect data from 303 Spanish patients (in two contexts: hospitalized and medical visit). Confirmatory factor analysis confirmed a one-factor solution in the 15-item (SCQesp) and five-item (SCQesp-SF) short form version. The SCQesp showed excellent values of reliability: Cronbach's α = 0.98; composite variance = 0.98 (0.905-0.854); and stratified variance = 0.78. The SCQesp-SF showed similar values of reliability. The SCQesp has excellent psychometric properties, making it a valid and reliable measure for assessing compassion in healthcare research and clinical care. This scientifically rigorous and psychometrically robust compassion measure in Spanish could allow healthcare providers, researchers, and leaders to routinely assess compassion.
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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 | 0.012 | 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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".