P888: A community-based approach to the reporting of secondary findings in Indigenous communities in Canada
Notice bibliographique
Résumé
Indigenous communities have historically been excluded from healthcare research, particularly in genomics. Their unique historical, cultural, and social contexts create distinct genetic landscapes, resulting in distinctive disease prevalence and susceptibility. Indigenous communities see higher rates of chronic conditions like diabetes, arthritis, and asthma, as well as rare genetic disorders like Spinobulbar muscular atrophy, demonstrating how understanding these differences is crucial to understand Indigenous health issues. Secondary findings (SFs) from Indigenous research participants can provide significant insights into health aspects for an entire community due to genetic relatedness. The aggregation of medically actionable SFs from individual research participants can have important implications on a community by signifying broader health trends within the community that can then be appropriately addressed by leadership. As such, a community-based approach to reporting SFs in Indigenous groups could improve Indigenous engagement in genomics research by prioritizing community values and information governance. Indigenous peoples view health in a community context, extending the benefits of genomic research beyond the individual to explain the differences in disease prevalence or susceptibility their communities face. Many Indigenous participants involve themselves in genomic research with hopes of improving community health knowledge and community health initiatives. Reporting SF information to communities aligns with this collectivist ideology, increasing available information about community health outcomes and concerns. Failing to broadly report these findings could also negatively impact community health by restricting access to actionable findings. Furthermore, community-based reporting of SFs allows for greater community autonomy and data governance. Maintaining decision-making autonomy over their data is an imperative in Indigenous communities due to a history of data misuse. By reporting SF data, decision-making powers are increased in the community, while they are also empowered to utilize and act on the information in ways that align with other cultural values. To ensure benefits to community health and appropriate data governance, community reporting of SFs would be required. However, this must involve active community engagement with leaders and professionals to design a system and level of control that would be appropriate. Specific attention should be put on how much and what kind of SFs would be beneficial to report. Those that are clinically actionable, or those where meaningful health/lifestyle interventions can be taken should take precedence. Moreover, meaningful steps would need to be taken to explore how to utilize such data to improve community health. Community access, involved governance, and iterative engagement remains vital to ensuring such steps can be taken. The emphasis on collectivist ideals does not negate the autonomy of individuals who would be partaking in genomic research, instead providing a framework where community disclosure is the default. As such, community leaders and academics could set bounds on aggregate information, and those not willing to share results could opt-out of the research. This opt-out approach has been successful in other applications, promoting participation in research studies as well as privacy, autonomy, and governance. Overall, a community-based approach to SF reporting may provide an alternative pathway for engaging Indigenous communities in genomic research by exercising community values increasing health knowledge, and improving decision-making power, leading to better health outcomes within Indigenous communities.
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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 | 0,021 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,002 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,002 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,001 |
| 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 ».