Cross-Cultural Adaptation and Application of the One-Parameter Item Response Model to the Santa Clara Brief Compassion Scale (SCBCS)
Bibliographic record
Abstract
Abstract Objectives International research has consistently demonstrated the positive impact of compassion towards others on both physical and mental well-being, with significant implications for mindfulness practice. Based on this evidence, we aimed to adapt the Santa Clara Brief Compassion Scale (SCBCS) into German while simultaneously conducting a cross-cultural validation and enhancing its measurement precision using Rasch methodology across samples from Germany, Ghana, India, and New Zealand. Method We applied the unrestricted Partial Credit Model to analyze data from a randomly selected subsample of 500 participants, drawn from a total convenience sample of 1822 individuals recruited from the general populations of Germany, Ghana, India, and New Zealand. Results Our initial analysis of the SCBCS showed significant misfit to the Rasch model ( χ 2 (30) = 58.48, p < 0.001), which was successfully addressed by testlet creation resulting in satisfactory model fit ( χ 2 (24) = 24.80, p = 0.09). This included strict unidimensionality, strong reliability (Person Separation Index = 0.81), and invariance across personal factors, such as country, educational levels, sex, and age. We then developed an algorithm for transforming ordinal scores to interval-level data to enhance the accuracy of the SCBCS. The scale demonstrated sound divergent and convergent validity. Conclusions Our study has validated both the German and English versions of the SCBCS using Rasch methodology. The precision of measuring compassion towards others using the two versions of the SCBCS can be further enhanced by applying the ordinal-to-interval transformation tables developed in this paper.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".