Family Spillover Effects of Marginal Diagnoses: The Case of ADHD
Bibliographic record
Abstract
The health care system commonly relies on information about family medical history in the allocation of screenings and in diagnostic processes. At the same time, an emerging literature documents that treatment for "marginally diagnosed" patients often has minimal impacts. This paper shows that reliance on information about relatives' health can perpetuate marginal diagnoses across family members, thereby raising caseloads and health care costs, but without improving patient well-being. We study Attention Deficit Hyperactivity Disorder (ADHD), the most common childhood mental health condition, and document that the younger siblings and cousins of marginally diagnosed children are also more likely to be diagnosed with and treated for ADHD. Moreover, we find that the younger relatives of marginally diagnosed children have no better adult human capital and economic outcomes than the younger relatives of those who are less likely to be diagnosed. Our analysis points to a simple adjustment to physician protocol that can mitigate these marginal diagnosis spillovers.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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 teacher head, 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".