Ways to Support Mental Health and Mental Well-being of Racialized and Immigrant Communities: A Concept Mapping Study
Notice bibliographique
Résumé
Introduction Although there is recent growing attention on mental health and mental well-being across the globe, supports in this area of healthcare can be a challenge for immigrant and racialized groups with frequent experiences of hardship. Objectives This study aimed to gather perspectives of immigrants and racialized community members on strategies central to support their mental health and well-being, with the aim of addressing research-to-practice gaps. Methods The study was co-designed in collaboration with a Community Action Table in Markham, Ontario, a setting with 93% of residents self-identifying as Canadian visible minorities (i.e., non-Caucasian descent). A mixed method Concept Mapping methodology was used to engage residents, service providers, and policymakers (n = 68) through three phases of data collection and interpretation. Results Participants first brainstormed ways to support their mental health and well-being, generating 283 statements in three group sessions. A consolidated list of 68 statements was then prepared by removing duplicates and merging similar ideas. This list was shared with participants in three group sessions for the sorting and rating actvities: each participant made groups of statements based on a shared meaning and labelled the groups; and rated each statement on a scale of 1-5 for its importance and feasibility to act in next six-months to support the mental health and well-being of their community. The sorted and rated data was then analyzed statistically through techniques of similarity index and hierarchical cluster analysis to produce visual maps, which were shared with participants in the interpretation session for review and naming of clusters followed by open discussion. This led to a 9-cluster concept map comprising of Family Wellness, Awareness & Education, Cultural Sensitivity, Social Service Access, Community Building, Socioeconomic, Food Security, Healthcare Access, and Housing Stability. The rating data showed the clusters of Family Wellness, Housing Stability, Healthcare Access, and Awareness & Education were ranked high for the dimension of importance. In terms of feasibility to act in next six-months, the clusters of Awareness & Education and Family Wellness remained among the top three while the clusters of Housing Stability and Healthcare Access scored low – which was discussed by participants as requiring a multi-year action plan with short- and long-term goals. Conclusions Overall, participants viewed mental health and well-being as being closely tied to their living and working conditions while also focusing on family wellness and intergenerational dynamics. The gained insights emphasize a need for multi-sectoral response to support the mental health and well-being supports of immigrant and racialized communities. Disclosure of Interest None Declared
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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 | 0,010 | 0,010 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,003 | 0,002 |
| Études des sciences et des technologies | 0,006 | 0,003 |
| Communication savante | 0,003 | 0,002 |
| Science ouverte | 0,001 | 0,005 |
| Intégrité de la recherche | 0,001 | 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 ».