Understanding Burdens and Barriers to Dermatological Management, and Potential for Virtual Care in Northern and Rural Canadian Indigenous Communities: A National Healthcare Practitioner Cross-Sectional Survey Analysis
Bibliographic record
Abstract
Northern and rural Canadian Indigenous communities (NRCIC) are well known to face health disparities, yet many remain formally not documented, including dermatologic challenges. We performed a mixed methods cross-sectional national survey of healthcare practitioners (HCPs) (n=50; mostly dermatologists, general practitioners, nurses, and pediatricians) to better understand the current NRCIC dermatology status and care needs, barriers to accessing dermatology and impacts of the COVID-19 pandemic, facilitators and barriers to virtual care, and practitioner recommendations. Most HCP participants identified NRCIC as underserviced in dermatologic care. HCPs reported atopic dermatitis and bacterial skin infections among the most common conditions, and consistently raised concerns for disproportionately severe and inadequately managed disease compared to urban populations. Barriers to accessing dermatology care in NRCIC broadly encompassed proximity to care, long wait times, inadequate supply and access to therapies, impractical and burdensome skin care regimens, socioeconomic and implementation barriers, transportation challenges, and cultural barriers. Although the COVID-19 pandemic helped establish virtual care for NRCIC to mitigate access-to-care issues, participants identified travel and cost-effectiveness barriers, concerns for poor infrastructure to sustain virtual care, inadequate photo quality, and lack of in-person care as persistent and growing problems. As future solutions, HCPs recommended increasing in-person dermatologist visits to remote communities, increasing educational dermatology care programs for rural HCPs, increasing use of virtual care to these communities, stimulating assistance (eg. coordinators) to facilitate care, and further cultural safety training. Future research addressing NRCIC, including direct input from community members, may help bridge dermatologic care gaps and improve health equity.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".