Translation and Validation of the Japanese Version of the Trait and State <scp>Post‐Event</scp> Processing Inventory
Bibliographic record
Abstract
Abstract In this study, we translated the Trait and State versions of the Post‐Event Processing Inventory (PEPI) into Japanese and examined their psychometric properties. One thousand participants, comprising three subsamples, completed the questionnaires. Confirmatory factor analyses supported a bi‐factor model, comprising one general factor and three theoretically derived subfactors (“Frequency,” “Self‐judgment,” and “Intensity”), for the State and Trait versions of the scale. However, both versions were essentially unidimensional, and scoring based on subfactors lacked support. Additionally, we found preliminary evidence for the test–retest reliability; internal consistency; and concurrent, convergent, divergent, incremental, and predictive validity of both versions. Furthermore, participants with self‐reported diagnoses of social anxiety disorder exhibited higher scores on both the PEPI‐Trait and PEPI‐State than healthy controls. Our findings suggest that the Japanese versions of the PEPI‐Trait and PEPI‐State may become useful alternatives to existing measures of post‐event processing in Japan.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".