Development of the Japanese Version of the State Self-Compassion Scale (SSCS-J)
Bibliographic record
Abstract
Research in the U.S. developed and validated the State Self-Compassion Scale (SSCS), which measures self-compassionate reactions toward a specific negative event. The current study is aimed at developing the Japanese version of the State Self-Compassion Scale (SSCS-J) and extending previous findings in the U.S. by showing measurement invariance across sexes and demonstrating the construct validity of this scale. Across two studies (n = 596 in Study 1, n = 474 in Study 2), the bifactor exploratory structural equation modeling representation of the SSCS-J showed excellent fit in which a single global factor (i.e., self-compassion) and most of the specific factors (six subscales) were well defined. Study 1 further provided evidence for the measurement invariance across sexes. The SSCS-J was related with higher trait self-compassion and lower fear of and negative beliefs about self-compassion. In Study 2, participants who were instructed to be self-compassionate reported higher scores in the SSCS-J relative to those in the control condition. These results attest to the replicability of the factor structure of the SSCS in Japan and provide further evidence for the construct validity of this scale.
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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 | 0.003 | 0.006 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".