Depressão materna, composição da família e pobreza: como estes fatores afetam a saúde da criança no primeiro ano de vida?
Bibliographic record
Abstract
Introduction: The child’s overall health depends on several factors, including the quality of the environment in which it lives and the care it receives. Child well-being early in life has an impact on its future and future generations’ health. Objective: Analyze the association of maternal depression, family composition and socioeconomic conditions with the indicator of maternal care and physical health of children. Methods: Retrospective cohort that analyzed data from 120 children in the first year of life. A Health and Maternal Care Indicator (ISCM) was created, aggregating information on growth, breastfeeding, vaccination, prophylaxis of iron deficiency anemia, illnesses and accidents. The socioeconomic and health conditions were obtained through a structured interview. Maternal depression was assessed by the Edinburgh Postnatal Depression Scale. The association between the ICSM and the predictors was examined by Quasipoisson Regression. The initial model was composed of variables with p<0.25 in the univariate analysis and p<0.05 in the final model. Results: The mothers were adults (83.3%), studied for an average of 10 years and 36% of them had depressive symptoms. About 37% of the families were single-parent female, 59% were from Class C1-C2 of ABEP and 12% received the “Bolsa Família” benefit. ISCM was 8% lower in children whose mothers were depressed (p = 0.04) or had no partner (p = 0.03), and was 14% higher in families receiving Bolsa Família (p = 0.02) in relation to their peers. Conclusion: Maternal depression and female single-parent family arrangements negatively impacted child health and care, while the conditional cash transfer program represented a protective factor.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".