Application of Repeated Measures Method to Compare Physical Maternal Health Index in a Follow-up Study
Bibliographic record
Abstract
Maternal health is a concept indicating the health of mothers during pregnancy, childbirth and the post-partum period. Recent literature indicates that the maternal mortality rate in Iran is 23 per 100,000 live birth. For every death, 20 to 30 mothers suffer from chronic and acute illnesses arising from pregnancy and delivery; therefore, using a valid and reliable indicator will help researchers assess maternal health more precisely. In this longitudinal study, attempt has been made to compare mothers’ health status by using a subjective tool for evaluating the Physical-Maternal Health Index (P-MHI). The most common complaints made by mothers were headache, back pain, mastitis, nipple fissure, breast abscess, abdominal pain, pain in genitalia, constipation, hemorrhoids, anal fissure, urinary problems/Incontinence, fatigue, and dizziness/vertigo. The P-MHI is defined on the basis of these complaints scaling 0-100. The higher the index, the better the health status of mothers. It was administered by structured interviews with 743 participants before and during pregnancy, one week, two, four, and six months after childbirth (2010-2011). Post-natal mothers experience various problems, with back pain and fatigue being the most common. The P-MHI was at its lowest level in the first week after delivery (76 out of 100), followed by the second month (82 out of 100). The results showed that not only do mothers significantly lose their reproductive health after pregnancy, they don't attain their pre-pregnancy health status even six months after delivery (P<0.0005). The trend of the P-MHI identified when and what kind of health care is the most urgent need of post-natal mothers.
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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 | 0.017 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".