Access and Utilization Patterns of School-Based Health Centers at Urban and Rural Elementary and Middle Schools
Bibliographic record
Abstract
OBJECTIVE: We examined patterns of enrollment, use, and frequency of use in school-based health centers (SBHCs), as well as the referral, diagnosis, and disposition of SBHC visits among newly implemented SBHCs. METHODS: Four rural and four urban school districts implementing SBHCs were examined from 2000 to 2003. Total school enrollment for students was 13,046. SBHC enrollment and medical encounter data were tracked using a Web-based medical database. Descriptive analyses were conducted to evaluate primary care access and utilization patterns. RESULTS: A total of 7,460 (57.2%) students were enrolled in their SBHCs, of which 4,426 used the SBHC at least once for a total of 14,050 visits. SBHC enrollment was greater in urban districts but rate of utilization was higher in rural districts. Black students, students with public or no health insurance, and students with asthma or attention deficit disorder had higher enrollment and utilization. Rural parents referred more children to SBHCs than urban parents. Teachers referred more students who were black, had asthma, had no public or health insurance, or had acute-type health issues. Total visits increased during the three years, with the largest increase in mental health services. Students who were younger, white, attended rural schools, had public or health insurance, or had infections were more likely to be sent home. Those with chronic conditions and visits for mental health were more likely to be returned to class. CONCLUSION: Utilization patterns suggest improved access to needed health care for disadvantaged children. SBHCs are an important part of the safety net for the populations they are intended to serve.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".