Factors Associated With Undernutrition Among Pregnant and Lactating Syrian Refugee Women in Jordan
Bibliographic record
Abstract
BACKGROUND: Maternal undernutrition is a public health issue and is reported to cause life-long and irreversible damage, with consequences at the individual, community, and national level. Many factors are reported to impact nutritional status for refugee pregnant or lactating women. Recently, Jordan has accepted an influx of refugees from Syria. Maternal undernutrition in pregnant and lactating Syrian women poses significant health risks.OBJECTIVE: To identify the relationship of undernutrition to underlying causes of socio-demographic, health and obstetric care, psychological wellbeing, social support, and marital violence among pregnant and lactating Syrian women attending obstetric outpatient clinics in Jordan.METHODS: The study was a cross-sectional assessment of 423 pregnant and lactating Syrian refugee women of established households within Jordan. Self-report questionnaires and anthropometric measurements were primary data sources.RESULTS: 49.2% (n=208) of participants were categorized as undernutrition (undernourished), a problem that is more prevailing among pregnant than lactating women. Statistical significance association was found for the variables extended family type, availability of health services, regular exercise, the trimester of pregnancy, low birth weight of the baby, and psychological well-being, when examined against undernutrition status.CONCLUSION: Undernutrition is a significant health issue among women of reproductive age. This study is a building block for further research, yet it provides basic information on the effect of undernourishment on pregnant and lactating Syrian refugee women.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".