Cultural adaptation: making prevention work across contexts / Lessons from Indigenous suicide prevention programmes implementation in Canada and the United States of America
Notice bibliographique
Résumé
Cultural knowledge, values, and practices, alongside social determinants of health, are essential to fostering individual and community well-being. Recognising the significance of local social contexts and cultural practices is especially critical when developing health interventions for Indigenous communities. For Indigenous Peoples, health outcomes are uniquely shaped by historical and ongoing impacts of colonisation, including residential schools, policies of cultural suppression and forced assimilation. These systemic factors have created significant health disparities, demanding that prevention programmes acknowledge both historical and contemporary structural issues at community- and national- levels while embedding Indigenous culture, knowledge, and values to bolster resilience. Despite the importance of culturally grounded approaches, few evidence-based programmes have been developed with Indigenous populations. Mainstream prevention strategies often face challenges related to cultural relevance when evidence-based programmes are scaled-out to other populations. Cultural adaptation has been seen as a promising avenue to transform health promotion and other prevention programmes. Cultural adaptation modifies content, language, and the mode of delivery of existing programmes to better fit marginalised community contexts and needs. Cultural adaptation for Indigenous communities is often conducted in a collaborative effort between academics and Indigenous community members to adapt evidence-based programmes to better align with Indigenous context. However, the processes of cultural adaptation and their practical implications remain poorly understood, particularly in the implementation space shared between programme teams and community partners, who then navigate different epistemologies, priorities, and dynamics embedded in layers of sociocultural, economic and politics forces. Without critical insight into these processes, cultural adaptation risks unintentionally reinforcing historical and systemic injustices. To bridge this knowledge gap, this thesis uses qualitative and participatory methodologies in a three-part analysis to explore the conceptualisation and implementation of cultural adaptation within two Indigenous suicide prevention programmes in Canada and the United States of America. Study one provides an overview of the collaborative adaptation of a community-based suicide prevention programme originally designed for Alaska Natives community members to mainly non-Indigenous school teachers and staff. The results highlight key shifts of programme core values to better align with the new context, including reframing the original emphasis on Alaska Natives self-determination to a focus on reflexivity and culturally humble action for the non-Indigenous participants. Study two details decentralised approaches to adaptation in a mental health programme for First Nations youth and care givers in Canada across four diverse communities. The results show that the decentralised approach, coupled with built-in flexibility in the conceptualisation of the implementation process, supported the empowerment of community partners to take the lead in adapting and designing context specific and culturally relevant programmes. Study three shares the backstage of a collaborative team effort to define core elements of a community-based suicide prevention programme designed with Alaska Natives, to prepare the programme to be adapted to other populations. The results show that the values embedded with core elements are crucial to defining the essence of the programme and thus need to be considered when adapting to other populations. This thesis finishes by synthesising the findings and lessons learned from all three studies, emphasising the potential and challenges of cultural adaptation, and the advances of knowledge in implementation science and health for, and with, Indigenous communities
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 | 0,015 | 0,017 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,002 | 0,003 |
| Études des sciences et des technologies | 0,029 | 0,012 |
| Communication savante | 0,010 | 0,004 |
| Science ouverte | 0,004 | 0,010 |
| Intégrité de la recherche | 0,002 | 0,008 |
| 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 ».