Teledermatology in remote Indigenous populations: Lessons learned and paths to explore, an experience from Canada (Québec) and Australia
Bibliographic record
Abstract
Objective: Recent introduction of a provincially funded and administered teledermatology platform in Quebec presents a major opportunity to improve healthcare delivery to rural Indigenous communities where healthcare is suboptimal. In this study, we assessed approaches, challenges, solutions, and outcomes in implementing teledermatology in rural Indigenous communities of Australia and Canada. Methods: A narrative review was performed using journal articles and grey literatures to assess challenges encountered in Canadian and Australian teledermatology programs in rural Indigenous communities. We then conducted a focused search to identify solutions and outcomes to these challenges. We identified four main areas of focus for implementing teledermatology: financial, cultural, legal, and provider competency. Results: Main financial concerns included identifying the cost-to-benefit ratio of teledermatology and financial benefits of the store-and-forward system compared to videoconferencing. Delivery of teledermatology through culturally considerate services is crucial to mend the mistrust felt by Indigenous people toward mainstream health services. From a legal standpoint, patient confidentiality and physician liability must be considered. A uniform teledermatology platform and physician competency in both telemedicine and dermatology are needed to ensure standard of care. Conclusion: Teledermatology initiatives represent great opportunities to improve healthcare services to rural Indigenous populations.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".