Trajectories of poverty and economic hardship among American families supporting a child with a neurodisability
Bibliographic record
Abstract
BACKGROUND: Caring for a child with a neurodisability (ND) impacts the financial decisions, relationships and well-being of family members, but evidence on the economic trajectories of families throughout the life course is missing. METHODS: Using data from the Panel Study of Income Dynamics, we tracked the families of 3317 children starting 5 years before childbirth until the child reached 20 years of age. We used regression and latent growth curve modelling to estimate trajectories of poverty and economic hardship over time. RESULTS: Families with a child with an ND had higher rates of poverty and economic hardship prior to childbirth and persistently over time. Analysis uncovered five latent trajectories for each indicator. After controlling for family and caregiver characteristics that preceded the birth of the child, raising a child with an ND was not associated with a unique trajectory of poverty. Families raising a child with an ND were however more likely to experience persistent economic hardship. CONCLUSIONS: The study establishes descriptive evidence for how having a child with an ND relates to changes in family economic conditions. The social and economic conditions that precede the child's birth seem to be driving the economic inequalities observed later throughout the life course.
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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 | 0.002 |
| 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".