Children's Mental Health over the Early Life Course: The Impact of Economic Resources, Neighborhood Disorder, and Family Processes
Bibliographic record
Abstract
Drawing upon a stress process and life course framework, and using data from the Child Supplement of the National Longitudinal Survey of Youth, the three papers presented in this dissertation examine the extent to which economic resources, neighborhood disorder, and family processes influence children’s trajectories of mental health.\nIn the first paper, I empirically construct six categories that represent children with comparable profiles of family income over time: increasing, decreasing, fluctuating, and stability across low-, medium-, and high-income families. The income categories are incorporated in multiple group latent growth curve models to assess the extent to which they initiate and shape children’s mental health trajectories from age 4 to 14. Results reveal significant disparities in antisocial behavior and depression/anxiety at age 4 and over time across the income categories.\nIn the second paper, I use these income categories to examine how stability and change in family income influences trajectories of maternal emotional support and the provision of cognitive stimulation in children’s home environments. In subsequent analyses, I examine the extent to which these different economic profiles moderate the relationship between family processes and children’s mental health trajectories.\nIn the third and final paper, I examine the relationship between stability and change in perceived neighborhood disorder and children’s trajectories of mental health. I conceptualize perceived neighborhood disorder as a two-part process involving a binary component that distinguishes between children exposed to minimal vs. high levels of disorder, and a continuous component that represents the actual level of disorder for children in the latter category. These two processes capture stability and change in neighborhood disorder over time, and are included in parallel process latent growth models to examine their separate and distinct impact on children’s trajectories of mental health.\nThe results from these papers underscore that the duration and sequencing of socioeconomic status, both at the family and neighborhood level, have important implications for children’s mental health and family processes. The results also underscore the complex and dynamic ways family processes influence children’s mental health in different economic contexts.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 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".