Staying Afloat: How Families Raising Children With Disabilities Used the Expanded Child Tax Credit
Bibliographic record
Abstract
While previous research has examined usage patterns and impacts of the expanded U.S. Child Tax Credit (CTC), less is known about how families raising children with disabilities responded to the CTC. While it is well-established that these families face greater financial constraints than other families, their utilization of such public programs remains underexplored. Using a novel, two-wave probability-based panel survey of more than 1,700 CTC recipients, this study investigates financial hardships faced by families raising children with disabilities, and their use of the CTC. Findings reveal high rates of financial hardship among these families, with most facing at least one to two hardships and nearly a quarter experiencing more. The study supports literature linking increased hardship to greater financial risks, healthcare costs, and routine expenses faced by families raising children with disabilities. Furthermore, it quantifies the multiplicity of hardships, illustrating that these families often face multiple challenges simultaneously. The results contribute to the understanding of financial vulnerability among families with children with disabilities and offer insights into potential benefits and limitations of policy interventions like the CTC. Future research directions and implications for practice are discussed, emphasizing the need for comprehensive support mechanisms tailored to the unique needs of these families.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".