Maternal health outcomes in the context of fragility: a retrospective study from Lebanon
Bibliographic record
Abstract
BACKGROUND AND AIMS: The Lebanese healthcare system faces multiple challenges including limited capacities, shortage of skilled professionals, and inadequate supplies, in addition to hosting a significant number of refugees. While subsidized services are available for pregnant women, representing the majority of the refugee population in Lebanon, suboptimal access to antenatal care (ANC) and increased maternal mortality rates are still observed, especially among socioeconomically disadvantaged populations. This study aimed to review the maternal health outcomes of disadvantaged Lebanese and refugee pregnant women seeking ANC services at primary healthcare centers (PHCs) in Lebanon. METHODS: A retrospective chart review was conducted at twenty PHCs in Lebanon, including Ministry of Public Health (MOPH) and United Nations Relief and Works Agency for Palestine refugees (UNRWA) facilities. Data was collected from medical charts of pregnant women who visited the centers between August 2018 and August 2020. Statistical analysis was performed to explore outcomes such as the number of ANC visits, delivery type, and onset of delivery, using bivariate and multivariable logistic regression models. RESULTS: In the study, 3977 medical charts were analyzed. A multivariate logistic regression analysis, revealed that suboptimal ANC visits were more common in the Beqaa region and among women with current abortion or C-section. Syrians had reduced odds of C-sections, and Beqaa, Mount Lebanon, and South Lebanon regions had reduced odds of abortion. Suboptimal ANC visits and history of C-section increased the odds of C-section and abortion in the current pregnancy. As for preterm onset, the study showed an increased likelihood for it to occur when being Palestinian, having current C-section delivery, experiencing previous preterm onset, and enduring complications at the time of delivery. CONCLUSION: This study suggests the need for low-cost interventions aiming at enhancing access to ANC services, especially among pregnant women in fragile settings.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 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".