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Identifying potential dropouts through school health records

N Swanson, B J Leonard

    The Journal of School Nursing : the Official Publication of the National Association of School Nurses
    |April 1, 1994
    PubMed
    Summary

    Incomplete school health records, particularly missing vision and scoliosis screening data, predict student dropouts. Attendance records combined with missing health data accurately identify at-risk students.

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    Area of Science:

    • Public Health
    • Educational Psychology
    • Health Informatics

    Background:

    • Student dropout rates pose significant challenges to educational and public health systems.
    • Accurate prediction of at-risk students is crucial for timely intervention.
    • School health records contain valuable data that may indicate potential dropout risks.

    Purpose of the Study:

    • To investigate the relationship between completeness of school health records and student dropout.
    • To identify specific health record variables that predict high school dropout.
    • To assess the accuracy of using health and attendance data for dropout prediction.

    Main Methods:

    • Retrospective analysis of school health records for 255 ninth-grade students in an inner-city Midwestern high school.
    • Comparison of health record completeness between students who dropped out and those who remained in school.
    • Statistical analysis to identify predictive variables for dropout.

    Main Results:

    • Students who dropped out were significantly more likely to have incomplete health information than students who stayed.
    • A combination of five variables (vision screening, scoliosis screening, health office visits, age, attendance) achieved over 85% accuracy in predicting dropouts.
    • Missing health data, when analyzed with attendance data, proved to be a strong predictor of potential dropouts.

    Conclusions:

    • Incomplete school health records are a key indicator of students at risk of dropping out.
    • Specific health screening data and attendance are powerful, accessible predictors of student dropout.
    • Utilizing existing school health and attendance data can facilitate early identification and intervention for at-risk students.

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