P-351 - Evidence of System-Based Stigma in the Hospital Reatment of Physical Disorders of Children and Adolescents with Psychiatric Disorders
Bibliographic record
Abstract
In this study we examine, across physician billing, ambulatory and inpatient/emergency data sets, the relationship between physical health and mental health costs among those with and without psychiatric disorders under the age of 18 years on their index vist. Visit data for all cases receiving specialized ambulatory, emergency or inpatient (tertiary) mental health services was constructed and subsequently matched on age and sex with comparisons in a final ratio of 1:8. Comparisons were health care users who did not receive treatment in the specialized tertiary public mental health system). Based on approximately 10 Million billing records, we compared the average number of visits and average costs per unique individual for “physical diagnoses” (non-psychiatric) and psychiatric diagnoses over the 16 year study period across case and comparison groups. We report among those with and without psychiatric disorders that physical disorders are significantly greater for those with any psychiatric disorder over the 16 year study period in both physician billing and ambulatory datasets. This result differs in the inpatient / emergency dataset in that cases have about 1/3 the number of admissions for physical diagnoses. It was unexpected that cases with a psychiatric diagnosis in the physician billing dataset had fewer physical disorder inpatient and emergency admissions. We suggest that this finding represents a form of system-based stigma.
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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 | 0.005 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".