Using administrative data to examine mental health service use among post-secondary students in Alberta, Canada
Notice bibliographique
Résumé
IntroductionPoor mental health among post-secondary students has been on the rise, and as such, has become a growing concern for the Alberta government. Alberta’s major post-secondary institutions have emphasized the need for evidence that would improve mental health supports for students troubled by mental health issues.
 Objectives and ApproachResponding to the need for evidence, the Child and Youth Data Laboratory profiled the socio-demographic characteristics (sex, socio-economic status, etc) of students who used mental health services between 2005/06 and 2010/11. In addition, using linked administrative data from a range of government programs, the profiles provide new data on the program involvement of post-secondary students who used mental health services, including educational achievement in high school, high cost health service use, the presence of chronic conditions, injury diagnoses, disability status, justice system involvement, income support, and type of mental health condition.
 ResultsOver the study period, 7% (~6,000) of post-secondary students received mental health services. Of those, between 11 and 13% were high cost health service users, ~20% received an injury diagnosis, and ~15% had a chronic condition. These proportions were higher compared to the proportions among students who did not receive mental health services. Rates of income support service use, corrections involvement, and students with disabilities were higher compared to students not receiving mental health services. A greater proportion of Canadian students (between 6.5% and 7.1%) compared to non-Canadian students (between 3.4% and 4.1%) received mental health services. In 2010/11, a greater proportion of part-time compared to full-time students were diagnosed with an anxiety disorder (3.4%, part-time; 2.3% full-time) or depression (4.0% part-time; 2.3% full-time).
 Conclusion/ImplicationsEvidence produced from linked administrative data offers a unique understanding of students who use mental health services, particularly in terms of their government program involvement. This new evidence can be used, for example, to determine if mental health service needs are different for Canadian versus non-Canadian students, or for full-time versus part-time students.
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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 | 0,003 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,005 |
| Science ouverte | 0,004 | 0,001 |
| 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.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».