The COVID-19 pandemic and maternal mental health in a fragile and conflict-affected setting in Tumaco, Colombia: a cohort study
Bibliographic record
Abstract
BACKGROUND: The effects of the COVID-19 pandemic on mental health have been understudied among vulnerable populations, particularly in fragile and conflict-affected settings. We aimed to analyse how the pandemic is related to early changes in mental health and parenting stress among caregivers, many of whom are internally displaced persons (IDP), in a conflict-affected setting in Colombia. METHODS: For this cohort study, we used longitudinal data from a psychosocial support programme in which 1376 caregivers were randomly assigned across four sequential cohorts. Recruitment of participants took place in March, 2018, for cohort 1; July, 2018, for cohort 2; March, 2019, for cohort 3; and July, 2019, for cohort 4. Participants completed assessments at baseline, 1-month, and 8-month follow-ups. The 8-month assessment occurred before the COVID-19 pandemic for participants in cohorts 1 and 2 (n=573), whereas those in cohorts 3 and 4 (n=803) were assessed during the early stages of the pandemic, 2-5 weeks after the national lockdown began on March 25, 2020. Primary caregiver anxiety and depression were measured with a scale adapted from the Symptoms Checklist-90-Revised and parenting stress was measured with the short form of the Parenting Stress Index. We estimated how mental health changed by comparing prepandemic and postpandemic 8-month outcomes using lagged-dependent variable models. FINDINGS: Results showed that the likelihood of reporting symptoms above the risk threshold increased by 14 percentage points for anxiety (95% CI 10-17), 5 percentage points for depression (0·5-9), and 10 percentage points for parental stress (5-15). The deterioration in mental health was stronger for IDP, participants with lower education or pre-existing mental health conditions, and for those reporting a higher number of stressors, including food insecurity and job loss. INTERPRETATION: Maternal mental health significantly worsened during the early stages of the pandemic. Considering the vulnerability and pre-existing mental health conditions of this population, the estimated effects are substantial. Policies in fragile and conflict-affected settings targeting IDP and other vulnerable people will be important to mitigate further mental health and socioeconomic problems. FUNDING: Saving Brains-Grand Challenges Canada, Fundación Éxito, Fundación FEMSA, United Way Colombia, Universidad de los Andes. TRANSLATION: For the Spanish translation of the abstract see Supplementary Materials section.
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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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".