Trajectories of Poverty and Economic Hardship among American Families Supporting a Child with a Neurodisability
Bibliographic record
Abstract
Caring for a child with a neurodisability (ND) impacts the financial decisions, relationships, and well-being of family members. Using the Panel Study of Income Dynamics (PSID), we tracked families from 5 years before child with ND birth until the child reached 20 years of age and used latent growth curve modeling to estimate different trajectories for risk of two indicators: poverty and economic hardship. In bivariate terms, families raising a child with ND had higher risks of poverty and economic hardship across time. Five latent growth trajectories were identified for each indicator. After controlling for family and caregiver characteristics that preceded the birth of the child with an ND, families raising a child with a ND were more likely to experience persistent economic hardship. However, raising a child with a ND was not associated with a unique poverty risk, suggesting that families already in poverty are more likely to remain poor if they have a child with a ND. The study establishes descriptive evidence for how having a child with a ND relates to changes in family economic conditions. The importance of social and economic conditions that precede the child’s birth lend support for a social causation framework of health inequalities.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".