Exploring Telehealth for Building Community Capacity and Well-being in Northern and Remote Indigenous Communities
Bibliographic record
Abstract
As technological systems play greater roles in bridging gaps in health care access and delivery for remote regions, it will be increasingly critical to identify innovative and successful digital health models that can lead to long-term sustainable programs. The adoption of telehealth solutions in northern and remote Indigenous communities are growing, however, implementation barriers and structural constraints from policies, resources and technological factors continue to affect the sustainability of programs and services. Previous research has tended to focus on the efficiency and cost-effectiveness of telehealth in facilitating healthcare, yet more work needs to be done to present a complete picture of users’ needs and perspectives in relation to the socio-cultural and technical factors shaping telehealth use in rural and remote Indigenous community contexts. This presentation examines the strengths and barriers for implementing telehealth technologies within Indigenous cultural contexts utilizing best practices based on an in-depth review and synthesis of academic, policy, and grey literature. I propose that the mutual shaping of technology and society approach serves as a path forward for exploring users’ perspectives and socio-cultural factors shaping the ways in which technologies are designed, implemented, and used, and alternatively how technologies affect our construction of social values and meanings.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".