Improving Access to Mental Health Care for Children: The Massachusetts Child Psychiatry Access Project
Bibliographic record
Abstract
BACKGROUND: Inadequate access to care for mentally ill children and their families is a persistent problem in the United States. Although promotion of pediatric primary care clinicians (PCCs) in detection, management, and coordination of child mental health care is a strategy for improving access, limitations in training, time, and specialist availability represent substantial barriers. The Massachusetts Child Psychiatry Access Project (MCPAP), publicly funded with 6 regional consultation teams, provides Massachusetts PCCs with rapid access to child psychiatry expertise, education, and referral assistance. METHODS: Data collected from MCPAP teams measured participation and utilization over 3.5 years from July 1, 2005, to December 31, 2008. Data were analyzed for 35,335 encounters. PCC surveys assessed satisfaction and impact on access to care. RESULTS: The MCPAP enrolled 1341 PCCs in 353 practices covering 95% of the youth in Massachusetts. The MCPAP served 10,114 children. Practices varied in their utilization of the MCPAP, with a mean of 12 encounters per practice per quarter (range: 0-245). PCCs contacted the MCPAP for diagnostic questions (34%), identifying community resources (27%), and consultation regarding medication (27%). Provider surveys revealed improvement in ratings of access to child psychiatry. The rate of PCCs who reported that they are usually able to meet the needs of psychiatric patients increased from 8% to 63%. Consultations were reported to be helpful by 91% of PCCs. CONCLUSIONS: PCCs have used and value a statewide system that provides access to teams of psychiatric consultants. Access to child mental health care may be substantially improved through public health interventions that promote collaboration between PCCs and child mental health specialists.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.003 |
| 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".