Disparities in pediatric psychosocial oncology utilization
Bibliographic record
Abstract
BACKGROUND: Integratedbehavioral health models have been proposed as care delivery approaches to mitigate mental health disparities in primary care settings. However, these models have not yet been widely adopted or evaluated in pediatric oncology medical homes. METHODS: We conducted a retrospective cohort study of 394 children with newly diagnosed cancer at Dana-Farber/Boston Children's Cancer and Blood Disorders Center (DF/BCH) from April 2013 to January 2017. Baseline sociodemographic characteristics and psychiatry utilization outcomes at 12 months following diagnosis were abstracted from the medical record. The severity of household material hardship (HMH), a concrete poverty exposure, at diagnosis and race/ethnicity were characterized by parent report using the Psychosocial Assessment Tool 2.0 (PAT). Associations between sociodemographic characteristics and receipt of psychiatry consultation were assessed with multivariable logistic regression models. RESULTS: Among 394 children, 29% received a psychiatric consultation within 12 months postdiagnosis. Of these, 88% received a new psychiatric diagnosis, 76% received a psychopharmacologic recommendation, and 62% received a new behavioral intervention recommendation. In multivariable logistic regression adjusting for age, cancer diagnosis, and PAT total score, there was no statistically significant association between HMH severity or household income and psychiatry utilization. Children who identified as racial/ethnic minorities were significantly less likely to receive a psychiatry consultation (OR = 0.48, 95% CI = 0.27-0.84). CONCLUSIONS: In a pediatric oncology medical home with an integrated behavioral health model, socioeconomic status was not associated with disparate psychiatry utilization. However, there remained a profound racial/ethnic disparity in psychiatry utilization, highlighting the need for additional research and care delivery intervention.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".