What's it gonna take? Lessons learned for youth-friendly mental health services research
Notice bibliographique
Résumé
Introduction: Over half of the children and youth with mental illness do not receive appropriate or adequate treatment in both Canada and the United States. The burden of mental illness and substance use is the leading cause of disability due to years lost to disability, leading to the youth mental health crisis. Despite ongoing efforts to improve mental health and substance use services, many youth disengage prematurely, with evidence that this leads to poorer outcomes. In this paper, we explore the question: how to use youth-friendly methods in research for service improvement. Methods: We used innovative and participatory action mixed methods. Youth between the ages of 12 and 25, with lived experience accessing mental health and addiction services, were recruited for focus groups. The focus groups were stratified based on their level of service needs, and data were analyzed using thematic analysis. The themes were interpreted into a fictional narrative summarized in an animated video. This video was embedded in a survey that was sent to the participants. The purpose was to validate the analysis and explore the factors that led them to participate. A descriptive analysis of the quantitative data and an inductive content analysis of the qualitative data were completed for the survey. Results: A total of 44 youth completed the screening to stratify the level of need. Fourteen youth participated in three pilot focus groups, and another 24 participated in four focus groups stratified by need. The mean age was 22.3 years, and 78% and 22% identified as male and female, respectively. Youth-friendly research was the main theme, with two main sub-themes: youth want to participate in research, and there were strategies for research approaches involving youth service users. Fundamentally, choice throughout the process was important. Conclusion: Youth service users want to be engaged meaningfully. Youth are not afraid to speak their truth and want opportunities to provide their unique perspectives. Service improvements from youth service-user feedback may lead to improved outcomes with full treatment because youth remain engaged with services. Service improvement may need youth-friendly research.
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 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 | 0,009 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,005 | 0,000 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,002 |
| 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 ».