COVID-19 and Women's Health: A Low- and Middle-Income Country Perspective
Bibliographic record
Abstract
Corona Virus Disease (COVID-19), a contagious disease, is a global pandemic affecting the lives and health of individuals across borders, genders and races. Much of what is known about the effects of natural disasters and disease outbreaks on women's health in particular, is based on studies conducted in high-income countries. The evolving evidence suggests that COVID-19 has a profound negative impact on the perinatal mental health of women. It is also clear that global pandemics such as COVID-19 disproportionately affect the less affluent, including individuals living in low- and middle-income countries. The purpose of this review is to summarize and critically discuss extant knowledge on COVID-19 as it relates to the perinatal health of women in low and middle-income countries, using Pakistan as a case example. We specifically highlight the effects on perinatal mental health, preterm birth, and timing of the COVID-19 exposure. Our review suggests that it is essential to consider the effects of COVID-19 within this cultural context and that findings from high-income countries do not necessarily translate to the situation in low and middle-income countries.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".