Impact of School-Based Health Centers on Students with Mental Health Problems
Bibliographic record
Abstract
OBJECTIVES: School-based health centers (SBHCs) play an increasingly major role in providing mental health services for students. This study evaluated the impact of SBHCs on mental health-care services and psychosocial health-related quality of life (HRQOL). METHODS: Four SBHC intervention and two matched non-SBHC school districts were examined from 1997 to 2003. The SBHC intervention began in 2000. Data included child and parent pediatric HRQOL and Ohio Medicaid claims. A longitudinal quasi-experimental time-series repeated measures design was used for this study, involving analysis of covariance to assess health costs and regression analyses for HRQOL scores. RESULTS: After the SBHC program, proportions of students accessing mental health-care services for urban and rural SBHC intervention schools increased 5.6% (chi2 = 39.361, p < 0.0001) and 5.9% (chi2 = 5.545, p < 0.0001), respectively, compared with increases of 2.6% (chi2 = 2.670, p = 0.1023) and 0.2% (chi2 = 0.006, p = 0.9361) for urban and rural non-SBHC schools, respectively. Using data from 109 students with mental health problems based on Medicaid claims, the study found SBHC students had significantly lower total health-care costs (F = 5.524, p = 0.005) and lower costs of mental health services (F = 4.820, p = 0.010) compared with non-SBHC students. While improvements over time in HRQOL for SBHC students compared with non-SBHC students and students from non-SBHC schools were observed, only some were statistically significant. CONCLUSIONS: SBHC programs increase the proportion of students who receive mental health services and may improve pediatric HRQOL. SBHC students with mental health problems had lower total Medicaid reimbursements compared with non-SBHC students.
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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 | 0.004 |
| 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 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".