The effect of the US Child Tax Credit advance payments in 2021 on adolescent mental health: Changes in depression symptoms and suicidality
Bibliographic record
Abstract
Objective: Child poverty is associated with poor adolescent mental health. Changes to the Child Tax Credit (CTC) in 2021 in the U.S. were historic and introduced a new model of distributing the credit in advance of tax filing, providing families with stable, supplemental monthly income. This policy shift offers a unique opportunity to examine the mental health effects for adolescents. Methods: We use electronic health record data from a large pediatric primary care network in Columbus, Ohio, which collected adolescent depression screening scores in real time as the CTC advance payments were introduced. We utilized differences in age of eligibility for the CTC to examine the changes in the probability of depression screening outcomes (positive depression screen, any depression symptom, any suicidal ideation), for adolescents eligible for the credit (turned 18 first quarter of 2022), relative to those not eligible (turned 18 last quarter of 2021) (n = 1,423). Results: We did not observe a significant association between the policy change and study outcomes in the overall sample. However, the percentage of adolescents with a positive depression screen significantly declined for Non-Hispanic Black (13.4 percentage point reduction, p = 0.01) and publicly insured (9.7 percentage point reduction, p = 0.04) adolescents. Conclusions: Our findings suggest reductions in depression symptoms for subgroups of adolescents who were age-eligible for the CTC compared to their counterparts who were not eligible. The CTC advance payments were a brief experiment in universal basic income and may offer a policy solution for addressing both poverty and a growing adolescent mental health crisis.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".