Towards a framework for the development, implementation and sustainability of eHealth interventions in Indigenous communities
Bibliographic record
Abstract
eHealth technology, an umbrella term including telemedicine, telehealth, and mobile health interventions (among others), has recently begun expanding its reach into Indigenous communities. With this new “migration” comes the need for special consideration of the factors that contribute to “successful” adoption, integration, and sustainability of such eHealth technologies in Indigenous communities. While existing frameworks are typically helpful orientations to guide eHealth implementation, they commonly lack elements that give specific consideration to the important nuances and special considerations when piloting eHealth initiatives in these unique and diverse community and cultural contexts. There is thus a need to expand, adapt, or design new eHealth adoption and implementation frameworks that help guide the piloting and use of health technologies in respectful, ethical, and community-centered ways in Indigenous communities. This paper suggests subjective considerations for the preliminary development of a generic eHealth technology adoption and implementation framework in Indigenous communities. Considerations are divided into three main sections: Development and Adoption; Implementation; and Sustainability, with relevant discussion of the centrality of community engagement, inclusivity, and respect. La cybersanté est une expression utilisée en médecine pour regrouper différentes technologies telles que la télémédecine, la télésanté, et les interventions de santé mobile. Avec une mise en oeuvre graduelle de la cybersanté dans les communautés autochtones, il y a des considérations spéciales et des facteurs spécifiques à prendre en compte pour assurer une intégration efficace et durable de ces technologies. Certaines infrastructures existent déjà pour faciliter l’utilisation de la cybersanté. Toutefois, il est important que l’utilisation de ces technologies soit éthique, respectueuse des différences culturelles autochtones, et en fonction des besoins des communautés autochtones. Cet article suggère des éléments à considérer dans le développement préliminaire d’une approche médicale axée sur la cybersanté et dans la mise en oeuvre d’infrastructure dans les communautés autochtones. Les considérations sont divisées en trois sections : le développement et l’adoption; la mise en oeuvre; et la durabilité, avec une discussion sur l’engagement communautaire, l’inclusion, et le respect mutuel.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.111 | 0.030 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.009 | 0.033 |
| Scholarly communication | 0.015 | 0.011 |
| Open science | 0.007 | 0.013 |
| Research integrity | 0.010 | 0.011 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".