The Effect of the COVID-19 Pandemic on Mental Health in Low and Middle Income Countries
Bibliographic record
Abstract
Abstract We track the effects of the COVID-19 pandemic on mental health in eight Low and Middle Income Countries (LMICs) in Asia, Africa, and South America utilizing repeated surveys of 21,162 individuals. Many respondents were interviewed over multiple rounds pre- and post-pandemic, allowing us to control for time trends and within-year seasonal variation in mental health. We demonstrate how mental health fluctuates with agricultural crop cycles, deteriorating during pre-harvest “lean” periods. Ignoring this seasonal variation leads to unreliable inferences about the effects of the pandemic. Controlling for seasonality, we document a large, significant, negative impact of the pandemic on mental health, especially during the early months of lockdown. In a random effects aggregation across samples, depression symptoms increased by around 0.3 standard deviations in the four months following the onset of the pandemic. The pandemic could leave a lasting legacy of depression. Absent policy interventions, this could have adverse long-term consequences, particularly in settings with limited mental health support services, which is characteristic of many LMICs.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".