Prenatal stress and child development: A scoping review of research in low- and middle-income countries
Bibliographic record
Abstract
INTRODUCTION: Past research has shown relationships between stress during pregnancy, and related psychosocial health measures such as anxiety and depressive symptoms, with infant, child, and adult outcomes. However, most research is from high-income countries. We conducted a scoping review to identify research studies on prenatal stress and outcomes of the pregnancy or offspring in low- and middle-income countries (LMICs), and to synthesize the stress measures and outcomes assessed, the findings observed, and directions for future research. METHODS: We searched PubMed, Scopus, and PsycINFO for English-language abstracts published from Jan 1960-Jan 2017. Search terms were related to stress and psychosocial health; pregnancy; infant or child development; and LMICs. RESULTS: 48 articles were identified. Sixty percent of studies were in upper-middle, 25% in lower-middle, and 15% in low income countries. Most studies used questionnaires, either existing or tailor-made, to assess stress. Eight assessed cortisol. Most studies (n = 31) assessed infant outcomes at birth, particularly gestational age or preterm birth (n = 22, 12 showing significant relationships), and birthweight (n = 21, 14 showing significant relationships). Five studies analyzed outcomes later in infancy such as temperament and motor development, all showing significant results; and nine in childhood such as behavioral development, asthma, and physical growth, with eight showing significant results. CONCLUSIONS: Results highlight the importance of prenatal stress on infant and child outcomes in LMICs. Methods used in high-income countries are successfully employed in LMICs, but tailored tools remain necessary. Careful assessment of covariates is needed to foster analyses of interactive effects and pathways. Studies including longer-term follow-up should be prioritized.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Systematic review | high |
| gpt | no category Domain: not available · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Systematic review | high |
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 | 0.010 | 0.043 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.023 | 0.026 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".