[Developing and Verifying the Validity and Reliability of the Electronic Menopausal Health Screen System].
Bibliographic record
Abstract
BACKGROUND: Menopausal women are in a transitional phase between health and sickness. Although the highest standards of menopausal care include clinical assessment and patient education on menopausal symptoms, current practices lack integrated care that aim to prevent chronic diseases for which menopause is a predisposing factor. PURPOSE: To integrate menopausal disturbances; to evaluate the risk factors for osteoporosis, cardiovascular disease, and diabetes; and to create a reliable and effective electronic menopausal health screen system (EMHSS). METHODS: The research was conducted in the four stages of assessment and analysis, design, development, and pretest stage in order to explore the effectiveness of the developed EMHSS. RESULTS: The EMHSS has a high degree of reliability and validity. Analysis found an expert validity of .97~.99, content validity of .99, and test-retest reliabilities of .80~.96 (Pearson's correlation) and .79~.96 (intra-class correlation). CONCLUSIONS / IMPLICATIONS FOR PRACTICE: The EPMHSS was developed using cross-disciplinary collaboration among nursing staff, medical practitioners, and information engineers in order to screen menopausal women. The EPMHSS provides tailored health education content for patients in a timely manner and compiles historical assessment data that may be referenced by nursing staff when providing health consultations and by physicians when delivering diagnoses and treatment.
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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 | 0.060 | 0.097 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".