Trends in maternal mental health during the COVID-19 pandemic–evidence from Zambia
Bibliographic record
Abstract
The COVID-19 pandemic has increased social and emotional stressors globally, increasing mental health concerns and the risk of psychiatric illness worldwide. To date, relatively little is known about the impact of the pandemic on vulnerable groups such as women and children in low-resourced settings who generally have limited access to mental health care. We explore two rounds of data collected as part of an ongoing trial of early childhood development to assess mental health distress among mothers of children under 5-years-old living in two rural areas of Zambia during the COVID-19 pandemic. We examined the prevalence of mental health distress among a cohort of 1105 mothers using the World Health Organization's Self-Reporting Questionnaire (SRQ-20) before the onset of the COVID-19 pandemic in August 2019 and after the first two infection waves in October-November 2021. Our primary outcome was mental health distress, defined as SRQ-20 score above 7. We analyzed social, economic and family level characteristics as factors modifying to the COVID-19 induced changes in the mental health status. At baseline, 22.5% of women were in mental health distress. The odds of mental health distress among women increased marginally over the first two waves of the pandemic (aOR1.22, CI 0.99-1.49). Women under age 30, with lower educational background, with less than three children, and those living in Eastern Province (compared to Southern Province) of Zambia, were found to be at highest risk of mental health deterioration during the pandemic. Our findings suggest that the prevalence of mental health distress is high in this population and has further worsened during COVID-19 pandemic. Public health interventions targeting mothers' mental health in low resource settings may want to particularly focus on young mothers with limited educational attainment.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".