Developing a Japanese version of the Injustice Experience Questionnaire-chronic and the contribution of perceived injustice to severity of menstrual pain: a web-based cross-sectional study
Bibliographic record
Abstract
BACKGROUND: Menstrual pain causes low quality of life among women of reproductive age, and often interferes with daily activities. Perceived injustice is a cognition linked to adverse symptoms. The aims of this study were to develop a Japanese version of the Injustice Experience Questionnaire-chronic (IEQ-chr-J), and to examine if perceived injustice is associated with pain intensity and impairment from menstruation. METHODS: We investigated 130 Japanese women (aged 20-45 years) with menstrual pain in the past 3 months using online self-administered questionnaires. We examined the psychometric properties of the IEQ-chr-J including: structural validity; internal consistency; and test-retest reliability (intra-class correlation coefficients; ICC). Concurrent validity was examined by correlations among the IEQ-chr-J, the Pain Catastrophizing Scale (PCS), the Hospital Anxiety and Depression Scale (HADS), a numerical rating scale (NRS) for maximum/average menstrual pain, and the Brief Pain Inventory (BPI) pain interference domain. We used multivariable regression analysis to investigate the association between perceived injustice and severity of menstrual pain, after excluding 10 hormone drug users. RESULTS: The IEQ-chr-J showed sufficient validity and reliability (Cronbach's α = 0.96, ICC 0.75, [95% confidence interval (CI): 0.61-0.88]. Pearson's correlation coefficients for the IEQ-chr-J, PCS, HADS anxiety, HADS depression, NRS, and BPI pain interference ranged from 0.27-0.65. The IEQ-chr-J was correlated with impairment due to menstrual pain (ICC 0.36, 95% CI: 0.14-0.58), an independent diagnosis of endometriosis, anxiety, and depression, but not with maximum or average pain intensity. CONCLUSIONS: The IEQ-chr-J has acceptable psychometric properties, and perceived injustice is associated with impairment from menstrual pain.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.001 | 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".