Translation and validation of the Chinese version of the menstrual distress questionnaire
Bibliographic record
Abstract
BACKGROUND: The Menstrual Distress Questionnaire (MDQ) is a commonly used questionnaire that assesses various symptoms and distress associated with the menstrual cycle in women. However, the questionnaire has not been completely translated into Chinese with rigorous reliability and validity testing. METHODS: This study translated the Menstrual Distress Questionnaire Form Cycle (MDQC) from English into Chinese: MDQCC in two stages. First, it was translated forward and backward using Jones' model; second, to test the validity and reliability, 210 Chinese-speaking women were recruited through online announcements and posters posted between June 2019 and May 2020. Expert validity, construct validity, convergent validity, and factorial validity were determined using content validity index (CVI), intraclass correlation coefficient (ICC), composite reliability (CR), and exploratory factor analysis, respectively. For concurrent criterion validity, MDQCC score was compared with three existing pain scales. Reliability was evaluated using internal consistency across items and two-week test-retest reliability over time. RESULTS: The CVI for content validity was .92. Item-CVI for expert validities among the 46 items ranged from .50 - 1; scale-CVI for the eight subscales, from .87 - 1; ICC, from .650 - .897; and CRs, from .303 - .881. Pearson correlation coefficients between MDQCC and short-form McGill pain questionnaire, present pain intensity, and visual analog scale scores were .640, .519, and .575, respectively. Cronbach's α for internal consistency was satisfactory (.932). ICC for test-retest reliability was .852 for the entire MDQCC. CONCLUSION: MDQCC was valid and reliable for Mandarin Chinese-speaking women. It can be used to evaluate female psychiatric symptoms related to the menstrual cycle in future work.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".