75 Disruptive Behaviour in Four Elementary Schools: Patterns of Disciplinary Referrals and School Responses
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,002 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».