Sociodemographic predictors of parenting stress among mothers in disadvantaged settings: evidence from rural and urban study sites in Kenya and Zambia
Bibliographic record
Abstract
Abstract Background: Parental stress occurs when parenting demands are greater than the resources available to cope with parenting. Previous research has identified household wealth, educational level, marital status, age, and number of dependent children as predictors of parental stress. However, limited evidence exists from sub-Saharan Africa (SSA). This study investigated the sociodemographic predictors of parenting stress among mothers in Kenya and Zambia. Methods: Data were obtained from longitudinal nurturing care evaluation studies conducted in rural and urban study sites in Kenya and Zambia. Mean parental stress scores (PSS) were compared across study sites, and multiple regression modelling was used to examine associations between sociodemographic predictors (household income, educational level, marital status, maternal age, child age, number of children aged <5 years) and PSS, adjusting for clustering and other predictors. Results: The mean PSS was lower in rural study sites and higher in urban sites (Kenya rural: 37.6 [SD=11.8], Kenya urban: 48.8 [SD=4.2], and Zambia rural: 43.0 [SD=9.1]). In addition, mothers’ income and educational level were associated with PSS (income: Kenya rural, β = -0.43; 95% CI[-16.07, -5.74]; P =.003**; Kenya urban, β = -0.33; 95% CI[-6.69, -0.80]; P =.01*; education: Kenya rural, β = -0.24; 95% CI[-8.97, -1.68]; P=.005**). Conclusion: Measures to increase education levels, alleviate poverty, and improve household incomes, such as subsidising childcare, improving parental stress levels, and positive parenting practices, lead to better growth and development of their children. Trial registration: PACTR201905787868050 and PACTR20180774832663
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".