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Record W4206794783 · doi:10.3389/fpsyg.2021.779318

Development of the Japanese Version of the State Self-Compassion Scale (SSCS-J)

2022· article· en· W4206794783 on OpenAlexaff
Yuki Miyagawa, István Tóth‐Király, Marissa C. Knox, Junichi Taniguchi, Yu Niiya

Bibliographic record

VenueFrontiers in Psychology · 2022
Typearticle
Languageen
FieldPsychology
TopicMindfulness and Compassion Interventions
Canadian institutionsConcordia University
FundersJapan Society for the Promotion of Science
KeywordsPsychologySelf-compassionScale (ratio)CompassionSocial psychologyCognitive psychologyClinical psychologyMindfulnessPolitical science

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.412
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0050.000

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.

Opus teacher head0.015
GPT teacher head0.299
Teacher spread0.284 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations26
Published2022
Admission routes1
Has abstractyes

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