Translation and validation of menopause quick 6 (MQ6) into the Malay language
Bibliographic record
Abstract
Abstract Background Inquiring conservative Asian women about their menopausal symptoms is often challenging in crowded primary healthcare clinics. Furthermore, the subject matter is culturally sensitive to most Malaysian women. Hence, the translation of the MQ6 into Malay is crucial to enable self-administration, eliminating the necessity for interviewers and mitigating potential respondent shyness. Methods The Menopause Quick 6 (MQ6) questionnaire was translated into the Malay language with an addition of an item, henceforth termed MQ6 (M). Forward and backward translation was performed. Face and content validity were conducted. MQ6 (M) was self-administered to 400 women aged between 40 and 60 attending six primary healthcare clinics in Malaysia. To ascertain the reliability for MQ6 (M), corrected Item-Total Correlation, Squared Multiple Correlation, Cronbach’s Alpha if the Item is Deleted, and Kuder-Richardson Reliability Coefficients (KR20). Exploratory factor analysis was done to determine its’ construct validity. Results The outcome of the validation was satisfactory. By the Lawshe method, the content validity ratios ranged from 0.6 to 1.0 and the content validity index was 0.914. The Internal consistency for the MQ6(M) Cronbach’s alpha was 0.711 while Kuder-Richardson Reliability Coefficients KR20 was 0.676. Factor loading of all four items is above 0.70, indicating a well-defined structure. Whereas factor loading for three items fell within the range of 0.50–0.69 indicating a practically significant threshold for a new questionnaire. Conclusion The MQ6 (M) has acceptable reliability and construct validity to be considered as a self-administered screening tool in primary care clinics in Malaysia.
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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 | 0.009 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".