Development of an Assessment Tool of Menstrual-Cycle-Related Signs and Symptoms Based on Thai Traditional Medicine Principles for Evaluation of Women’s Health
Bibliographic record
Abstract
Background. Utilization of Thai traditional medicine (TTM) was considered in menstrual-cycle-related signs and symptoms (MCSs) to evaluate women’s health. TTM clinicians diagnosed the MCSs by signs, symptoms, and associated factors of patients including a physical examination to find patterns of imbalance elements and the origin of the disorder to optimize treatment. Thus, the purpose of this study was to develop a new assessment tool, the menstrual-cycle-related signs and symptoms questionnaire (MCSQ) based on TTM principles for evaluation of women’s menstrual health. Methods. The items and components of the MCSQ were adjusted by TTM expert consensus using the Delphi technique. The content validity of the MCSQ was quantified by the content validity index (CVI). MCSQ were examined by construct validity and internal consistency reliability using exploratory factor analysis (EFA) and Cronbach’s α coefficient, respectively. Results: All 19 experts (100%) responded to the questionnaires in the three rounds of the Delphi technique. The MCSQ showed high content validity of individual items (I-CVI = 0.83–1.00) and high overall content validity of the questionnaire (S-CVI/AVE = 0.98). Overall, 429 of 432 participants completed the questionnaire (99.31%). After factor analysis, the final MCSQ was divided into two sections, which consisted of 49 items. The first had 23 items focusing on the MCSs. And, the second had 14 items of personal and medical data including 12 items of associated factors. Cronbach’s α coefficient of the final MCSQ was 0.87, and that of each component was between 0.32 and 0.82. Conclusions. This study reports a new MCS questionnaire tool, which was developed from TTM knowledge to evaluate women’s health. This questionnaire showed an acceptable level of validity and reliability. Thus, it is also expected to be useful in clinical practice and ongoing research on evaluation of women’s health.
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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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.001 | 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 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".