Understanding Resilience and Mental Well-Being in Southwest Indigenous Nations and the Impact of COVID-19: Protocol for a Multimethods Study
Notice bibliographique
Résumé
BACKGROUND: Despite experiencing many adversities, American Indian and Alaska Native populations have demonstrated tremendous resilience during the COVID-19 pandemic, drawing upon Indigenous determinants of health (IDOH) and Indigenous Nation Building. OBJECTIVE: Our multidisciplinary team undertook this study to achieve two aims: (1) to determine the role of IDOH in tribal government policy and action that supports Indigenous mental health and well-being and, in turn, resilience during the COVID-19 crisis and (2) to document the impact of IDOH on Indigenous mental health, well-being, and resilience of 4 community groups, specifically first responders, educators, traditional knowledge holders and practitioners, and members of the substance use recovery community, working in or near 3 Native nations in Arizona. METHODS: To guide this study, we developed a conceptual framework based on IDOH, Indigenous Nation Building, and concepts of Indigenous mental well-being and resilience. The research process was guided by the Collective benefit, Authority to control, Responsibility, Ethics (CARE) principles for Indigenous Data Governance to honor tribal and data sovereignty. Data were collected through a multimethods research design, including interviews, talking circles, asset mapping, and coding of executive orders. Special attention was placed on the assets and culturally, socially, and geographically distinct features of each Native nation and the communities within them. Our study was unique in that our research team consisted predominantly of Indigenous scholars and community researchers representing at least 8 tribal communities and nations in the United States. The members of the team, regardless of whether they identified themselves as Indigenous or non-Indigenous, have many collective years of experience working with Indigenous Peoples, which ensures that the approach is culturally respectful and appropriate. RESULTS: The number of participants enrolled in this study was 105 adults, with 92 individuals interviewed and 13 individuals engaged in 4 talking circles. Because of time constraints, the team elected to host talking circles with only 1 nation, with participants ranging from 2 to 6 in each group. Currently, we are in the process of conducting a qualitative analysis of the transcribed narratives from interviews, talking circles, and executive orders. These processes and outcomes will be described in future studies. CONCLUSIONS: This community-engaged study lays the groundwork for future studies addressing Indigenous mental health, well-being, and resilience. Findings from this study will be shared through presentations and publications with larger Indigenous and non-Indigenous audiences, including local recovery groups, treatment centers, and individuals in recovery; K-12 and higher education educators and administrators; directors of first responder agencies; traditional medicine practitioners; and elected community leaders. The findings will also be used to produce well-being and resilience education materials, in-service training sessions, and future recommendations for stakeholder organizations. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/44727.
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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 | 0,052 | 0,046 |
| Méta-épidémiologie (sens strict) | 0,003 | 0,004 |
| Méta-épidémiologie (sens large) | 0,004 | 0,004 |
| Bibliométrie | 0,005 | 0,004 |
| Études des sciences et des technologies | 0,008 | 0,004 |
| Communication savante | 0,004 | 0,005 |
| Science ouverte | 0,004 | 0,004 |
| Intégrité de la recherche | 0,005 | 0,008 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,072 | 0,013 |
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 ».