Conducting a Global Quadruple Aim Thematic Analysis of Telemedicine Performance in Rural Indigenous Populations and Evidence-Based Recommendations for Improvement
Bibliographic record
Abstract
As telehealth is a growing form of healthcare delivery across the world, particularly after the COVID-19 pandemic, it’s impact on patient populations particularly in aboriginal and rural communities boasts many questions. As the health disparities between aboriginal groups living in rural areas on reserves and the rest of the Canadian demographics remain to be mountainous, telemedicine is often seen as the new way forward in reducing these healthcare gaps. Presently, much research has been conducted on these cohorts, particularly in the health equity atmosphere. However, much of this research lacks a comprehensive framework or tool in which it analyzes the efficacy of outcomes. In this review paper, the quadruple aim – the ideal standard of care which North American health systems seek to conform to – will be used to analyze telemedicine performance, and assert evidence-based recommendations for improvement. Therefore, this paper seeks to conduct a thematic analysis on the various issues and barriers to telemedicine delivery and usage in aboriginal populations with respect to the quadruple aim as well as identifying evidence-based solutions to alleviate some of these concerns and bolster care.
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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 | 0.145 | 0.112 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".