Use and perceived effectiveness of multidisciplinary teams to address problematic student behaviour to prevent campus violence in Canadian higher education
Notice bibliographique
Résumé
Case studies of high-profile occurrences of on-campus violence have resulted in recommendations for colleges and universities to implement multidisciplinary teams, called Behavioural Intervention Teams (BITs). These teams serve as a mechanism to collect, assess, and intervene when high-risk behaviours occur within an institution and prevent future violence. BITs have been in operation in the United States for over a decade and, thus this study sough to understand to what degree Canadian institutions have implemented teams. Subsequently, this study was designed to understand the experience of those who serve on such teams and their perceptions of the effectiveness of the practice. This multi-staged mixed methods study distributed online surveys, adapted from previous American surveys (Gamm, Mardis, & Sullivan, 2011; Van Brunt, Sokolow, Lewis, & Schuster, 2012), to all English-speaking institutions in Canada and a representative sample of team members were interviewed. All results were analyzed using the social ecological model which is a recommended approach when conducting effective violence prevention work. Nearly 75% of Canadian institutions have implemented teams, which had been in operation for an average of just over four years. It was found that the larger an institution the more likely the institution was to have a team. The characteristics of Canadian teams did not differ drastically from the characteristics of United States teams with the exception of team function and meeting frequency as Canadian teams had adopted a practice of co-leadership. Without question, team members described the BIT process as being an effective way to address problematic student behaviour as a method to prevent campus violence. Team members attribute the effectiveness to the inclusion of multidisciplinary perspectives within the membership of the team and how the backgrounds of each team member enhanced the ability of the team to appropriately assess and achieve a successful outcome. Despite the process of behavioural intervention being described as effective, team members articulated substantial challenges they experience in conducting their work: (a) team issues, (b) institutional issues, (c) case complexity, and (d) legal/policy issues. Team members also described how participating on a BIT team can have negative impacts on the individual professionally as a result of the additional workload associated with participating on the team. Team members described being negatively impacted personally as the work of BIT caused: (a) stress and fear, (b) interpersonal issues as a result of difficult team dynamics, and (c) negatively skewing their perceptions of the amount of distressed students within the institution. These negative impacts were countered by the overwhelming positive benefits that team members experienced as a result of their participation on a BIT team. Team members described professional benefits as: (a) trusted peers, (b) new skills, and (c) a greater sense of fulfilment within their role within the institution. Overall, team members described participating on a BIT team as enjoyable and held a strong belief that the work of BITs makes a difference within their campus community by maintaining a safe environment and how the work positively affects the student of concern by permitting them to continue their studies.
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Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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 tête enseignante, pas un consensus.
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