Perinatal Distress in Women in Low- and Middle-Income Countries: Allostatic Load as a Framework to Examine the Effect of Perinatal Distress on Preterm Birth and Infant Health
Bibliographic record
Abstract
In low- and middle-income countries (LMIC), determinants of women's and children's health are complex and differential vulnerability may exist to risk factors of perinatal distress and preterm birth. We examined the contribution of maternal perinatal distress on preterm birth and infant health in terms of infant survival and mother-infant interaction. A critical narrative and interpretive literature review was conducted. Peer-reviewed electronic databases (MEDLINE, Embase, Global Health, CINHAL), grey literature, and reference lists were searched, followed by a consultation exercise. The literature was predominantly from high-income countries. We identify determinants of perinatal distress and explicate changes in the hypothalamic-pituitary-adrenal axis, sympathetic, immune and cardiovascular systems, and behavioral responses resulting in pathophysiological effects. We suggest cultural-neutral composite measures of allostatic mediators (i.e., several biomarkers) of maternal perinatal distress as objective indicators of dysregulation in body systems in pregnant women in LMIC. Understanding causal links of maternal perinatal distress to preterm birth in women in LMIC should be a priority. The roles of allostasis and allostatic load are considered within the context of the health of pregnant women and fetuses/newborns in LMIC with emphasis on identifying objective indicators of the level of perinatal distress and protective factors or processes contributing to resilience while facing toxic stress. We propose a prospective study design with multiple measures across pregnancy and postpartum requiring complex statistical modeling. Building research capacity through partnering researchers in high-income countries and LMIC and reflecting on unique ethical challenges will be important to generating new knowledge in LMIC.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".