The association between music education in high school and academic and mental health outcomes among grade 12 students in Winnipeg, Manitoba from 2009/10-2017/18
Notice bibliographique
Résumé
Introduction: Educators contend that music programs benefit students, in terms of both academic achievement and mental wellness. Most studies assessing the relationship between music education and adolescent outcomes focus on academic achievement and have found mixed results. Although there is some evidence of the benefits of music programs to mental wellness, research looking at diagnoses of mental disorders related to music education is lacking, as is research within the Canadian context. Objectives: This retrospective cohort study investigated the association between being enrolled in high school for-credit music courses and i) academic outcomes and ii) mental disorder diagnoses while adjusting for several health and social covariates. The potentially moderating effects of sex and income level were also investigated. Methods: Administrative data housed by the Manitoba Centre for Health Policy (MCHP) were linked together to examine the relationship between the number of music courses taken and grade 12 mathematics and language arts achievement test marks, on-time graduation rates, and diagnosis with a mental disorder (ADHD, substance use disorder, mood or anxiety disorder, or any of these three mental disorders). To examine the risk of diagnosis after exposure to music courses, students with a diagnosis prior to grade 9 were excluded from the appropriate analyses. Grade 12 achievement tests were analyzed using multiple linear regression, on-time graduation using logistic regression, and mental disorder diagnoses using survival analysis. All analyses adjusted for several student characteristics including sex, income level, residential mobility, income assistance, involvement with child protection services, and prior academic achievement. Results: Taking any music courses was statistically significantly (p<.05) associated with higher marks on grade 12 mathematics and language arts achievement tests. The odds of graduating on time were also positively associated with taking music courses. This relationship was stronger for female students than male students. Taking 3 or more music courses was associated with a decreased risk of being diagnosed with a substance use disorder. For ADHD diagnosis rates, the only statistically significant difference was for students taking 1-2 music courses compared with non-music students. No association was found between music courses and mood or anxiety disorders. Moderating effects of sex and income level were found in some relationships between music courses and academic outcomes, but none were found in the relationships between music courses and mental disorders. Conclusions: Taking music courses was associated with better academic outcomes for grade 12 achievement tests as well as on-time graduation rates. Findings were mixed regarding the statistical significance of the association with a mental disorder diagnosis – taking music courses was associated with a decreased risk of a substance use disorder or ADHD diagnoses, but there was no relationship with a diagnosis of a mood or anxiety disorder. The statistical significance of the relationship of sex and income level with these findings varied across academic and mental disorder outcomes. These results suggest promising benefits of music courses to academic and mental health outcomes and point to continued support of music courses in Manitoba schools. Sex and income levels should be considered when planning music courses, given the moderating effects of sex and income levels found in some relationships between music courses and academic outcomes. Further research should continue to consider the underlying factors of the relationship between music courses and student outcomes, especially those related to mental disorder diagnoses as there is a paucity of research in this area.
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 | 0,001 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,001 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,002 | 0,003 |
| Études des sciences et des technologies | 0,002 | 0,001 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,002 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 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 ».