75 Disruptive Behaviour in Four Elementary Schools: Patterns of Disciplinary Referrals and School Responses
Bibliographic record
Abstract
Schools respond to disruptive behaviour (DB) using internal and external resources including referral to internal student support teams, functional and diagnostic assessments and requests for extra resources such as teacher's aids. We hypothesized that disciplinary referrals from teachers to the school office would decrease following school interventions. The four participating schools served disadvantaged neighbourhoods in Halifax, Nova Scotia and comprised 1,541 students from grades Kindergarten to Six. In order to assess the influence of factors on the reduction of referrals in the spring compared to the fall, we used multiple regression with the dependent variable being the difference in disciplinary referrals between fall and spring terms. The proportion of students who had one or more referrals to the school office during the 2001/2002 academic year ranged from 20 to 54% among the four schools. Among the 1,541 students, 37 had a current Individualized Education Program (IEP) because of behaviour difficulties. Seventy-six percent of students with IEPs had at least one disciplinary referral to the office during the academic year compared to 29% of students without a behavioural IEP (p<0.001) The mean number of disciplinary referrals for students who had behavioural IEPs was 2.1 in the fall and 2.5 in the spring compared to 0.46 in the fall and 0.41 in the spring for 1,504 students without a behavioural IEP. The increase in mean referrals from fall to spring in those with behavioural IEPs was not statistically significantly different from the decrease seen in those without behavioural IEPs (p=0.5). The rate of mental health diagnoses recorded in student records was 1.6%. Among the 25 students with known mental health diagnoses, 14 had ADHD or ODD alone or with a comorbid diagnosis. Schools differed greatly in predictors for decreasing disciplinary referrals and no single factor could explain decreases in all schools. Trajectories of disciplinary referrals for students with problems cannot be easily altered within one to two years and explanatory factors are not easily generalizable from one school to another. The rate of known mental health diagnoses is approximately an order of magnitude lower than rates predicted by epidemiological surveys. We are currently analyzing the impact of interventions during 2001/2002 on disciplinary referral rates during the subsequent year and will include these additional results in the presentation.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".