Developing and Evaluating a Child Maltreatment Training Program for Elementary School Teachers
Notice bibliographique
Résumé
Child abuse and neglect (i.e., maltreatment) affect a considerable number of children and can negatively impact how children learn and behave in the classroom. Knowledge gaps, false beliefs, and limited skills with regard to child maltreatment are commonly found among teachers, despite their vital role in the lives of maltreated children. In Canada, little has been done to develop training or other supports for teachers around detecting and responding to child maltreatment. This two-study dissertation brings awareness to the training needs of Canadian-based teachers regarding child maltreatment, as well as to the feasibility of developing, implementing, and evaluating child maltreatment teacher training. Study 1 involved a multi-informant assessment of (1) teachers’ child maltreatment training needs and (2) factors that may influence teachers’ training participation and use of maltreatment-related knowledge and skills. Using surveys and focus groups, qualitative information was gathered from three informant groups in a major urban region in Ontario (i.e., 19 school social workers, 9 child welfare practitioners, and 21 foster caregivers). Study 1 findings highlighted gaps in maltreatment-related knowledge and skills among local elementary school teachers in various areas related to child maltreatment detection and intervention (e.g., trauma-sensitive classroom practices, reporting). Beyond identifying training content needs, Study 1 results also identified multi-level factors that might influence teachers’ participation in child maltreatment training, as well as their use of maltreatment-related knowledge and skills. Most commonly, factors included teacher workload issues, teacher beliefs (e.g., negative attitudes toward child welfare), school internal supports (e.g., unavailability of social workers), and school resource structures (e.g., unmet demand for assessments). Building on these results, Study 2 designed, piloted, and evaluated a child maltreatment training curriculum for teachers. The training evaluation followed Kirkpatrick’s (1994) model for evaluating training programs, wherein the first three of four levels were addressed: (1) reaction (participants’ perceptions about the training); (2) learning (change in knowledge and/or attitudes); and (3) behaviour (change in actual work practices). A quasi-experimental design was used to gather data from a sample of 45 local teachers (19 intervention and 26 wait-list comparison group participants) with total experience in education ranging from 2 to 39 years. To explore participants’ feedback on the training, quantitative and qualitative information was gathered using surveys administered during the training (n = 19). To explore preliminary training outcomes, quantitative and qualitative information was gathered using pre- and post-training questionnaires (n = 45), as well as post-training interviews (n = 13). Training feedback findings supported the usefulness and relevance of training content for local elementary school teachers. Study 2 results also suggest that relatively brief child maltreatment training has the potential to improve teachers’ attitudes, knowledge, and likelihood of responding effectively to maltreatment-related issues at school. Although replication with additional samples and more rigorous methodology are needed, these findings represent an important first step in establishing improved training for Canadian school professionals regarding child maltreatment. Collectively, dissertation results also have several research and clinical implications. Foremost, this dissertation offers recommendations for future maltreatment training development, implementation, and evaluation efforts that include teachers and other school-based professionals. Dissertation results also highlight a need for complementary efforts to explore and address the intersection of factors and systems that may influence teachers’ participation in maltreatment training and research, as well as their responses to child maltreatment at school.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
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 | 0,006 | 0,009 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,003 | 0,001 |
| Communication savante | 0,002 | 0,001 |
| Science ouverte | 0,002 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 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 ».