Impact of School Nurse Case Management on Students with Asthma
Bibliographic record
Abstract
This project determined asthma prevalence in a large school district, absentee rates, and potential effects of school nurse case management for student asthma over three years. Data were derived from an asthma tracking tool used by nurses in one school district for every student reported as having asthma by their parent. School nurses began collecting data in their schools in 1999-2000 when an asthma-management protocol was first developed. Nurses documented perceived asthma severity for each student, presence of medication and peak flow meters in school, and case management activities provided. This data base was cross matched with percentage of days students were absent for any illness. Prevalence of asthma, based on school nurse records of parent report, was between 5.1% to 6.2% during the three years. Between 13.5% and 15% were moderate or severe. Students with asthma were absent between one-half to one and one-quarter days more often than those without asthma. In year three, 39% of students with asthma had medication at school, and 12% had a peak flow meter. Contacting a parent was the nurse case management activity provided for the largest number of students (27% of students with asthma), followed by asthma education (16.5%), contact with physician (6%), and home visits (1%). Students who received at least one school nurse case management intervention were more likely the next year to have an asthma medication at school, to use a peak flow meter at school, and to have a change in asthma severity. School nurse case management activity had no association with student absences. Availability of medication and peak flow meters at school was low, suggesting standards of care for asthma were not followed. School nurse case management, when performed outside a project or intervention, offers a promising strategy to improve asthma management.
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 | 0.004 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".