Self-Compassion Around the World: Measurement Invariance of the Short Form of the Self-Compassion Scale (SCS-SF) Across 65 Nations, 40 Languages, Gender Identities, and Age Groups
Bibliographic record
Abstract
Abstract Objectives The 12-item Self-Compassion Scale–Short Form (SCS–SF) is a widely used instrument for the assessment of self-compassion. To date, there have been few examinations of this instrument’s psychometric properties, particularly across nations and languages. Therefore, we used data from the Body Image in Nature Survey (BINS) to assess measurement invariance of the SCS–SF across nations, languages, gender identities, and age groups. Methods Participants (N = 56,968) from 65 nations completed the SCS–SF in 40 languages. Using these data, we tested various hypothesised models of the SCS–SF in the total sample and, using multi-group confirmatory factor analysis, tested for invariance of the optimal model across national groups, languages, gender identities, and age groups. Results In the total dataset, we found that an 11-item, 2-factor model (i.e., SCS-11) provided best fit to the data, with the two factors tapping distinct constructs of compassionate and uncompassionate self-responding. The SCS-11 was found to be partially scalar invariant across national groups and languages, and fully scalar invariant across gender identities and age groups. There was wide variation in latent means for the two factors, particularly across national groups and languages. Further analyses showed negligible associations between the two factors and sociodemographic variables, including marital status, financial security, and urbanicity. Conclusions Our results suggest that it may be possible to derive a stable 2-factor model of the SCS–SF for use in cross-cultural research, but also highlight the likelihood of cross-national and cross-linguistic variations in the way that self-compassion is understood.
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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 | 0.005 | 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".