Assessing factors associated with poor maternal mental health among mothers of children born small and sick at 24–47 months in rural Rwanda
Bibliographic record
Abstract
BACKGROUND: Global investments in neonatal survival have resulted in a growing number of children with morbidities surviving and requiring ongoing care. Little is known about the caregivers of these children in low- and middle-income countries, including maternal mental health which can further negatively impact child health and development outcomes. We aimed to assess the prevalence and factors associated with poor maternal mental health in mothers of children born preterm, low birthweight (LBW), and with hypoxic ischemic encephalopathy (HIE) at 24-47 months of age in rural Rwanda. METHODS: Cross-sectional study of children 24-47 months born preterm, LBW, or with HIE, and their mothers discharged from the Neonatal Care Unit (NCU) at Kirehe Hospital between May 2015-April 2016 or discharged and enrolled in a NCU follow-up program from May 2016-November 2017. Households were interviewed between October 2018 and June 2019. Mothers reported on their mental health and their child's development; children's anthropometrics were measured directly. Backwards stepwise procedures were used to assess factors associated with poor maternal mental health using logistic regression. RESULTS: Of 287 total children, 189 (65.9%) were born preterm/LBW and 34.1% had HIE and 213 (74.2%) screened positive for potential caregiver-reported disability. Half (n = 148, 51.6%) of mothers reported poor mental health. In the final model, poor maternal mental health was significantly associated with use of violent discipline (Odds Ratio [OR] 2.29, 95% Confidence Interval [CI] 1.17,4.45) and having a child with caregiver-reported disability (OR 2.96, 95% CI 1.55, 5.67). Greater household food security (OR 0.80, 95% CI 0.70-0.92) and being married (OR = 0.12, 95% CI 0.04-0.36) or living together as if married (OR = 0.13, 95% CI 0.05, 0.37) reduced the odds of poor mental health. CONCLUSIONS: Half of mothers of children born preterm, LBW and with HIE had poor mental health indicating a need for interventions to identify and address maternal mental health in this population. Mother's poor mental health was also associated with negative parenting practices. Specific interventions targeting mothers of children with disabilities, single mothers, and food insecure households could be additionally beneficial given their strong association with poor maternal mental health.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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".