MENTAL HEALTH ON THE MOVE: THE EFFECTS OF RELOCATION ON THE PSYCHOLOGICAL AND BEHAVIORAL HEALTH OF MILITARY CHILDREN
Bibliographic record
Abstract
Using rich panel data linking medical records of children in military families to their sponsor's service records, I estimate the effects of moving on psychological and behavioral health diagnoses of children. To identify causal effects, I employ an event study framework that leverages variation in treatment timing among children. I find significant short-term disruption effects in the quarter of a move, with up to a 10% reduction in diagnoses. Younger children (under 10) experience a larger initial decline in diagnoses, followed by a sharp increase in diagnoses in subsequent quarters. In contrast, children aged 10 or older exhibit a smaller initial decline compared to younger children, but their disruption period lasts for an additional quarter after the move. I do not find strong evidence of long-term effects of moving on psychological and behavioral diagnosis rates in children. Diagnosis rates generally return to pre-trend levels once children are established in their new location. Overall, my findings suggest that while moving temporarily disrupts diagnoses, the long-term effects on children's psychological or behavioral health diagnoses appear to be minimal.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".