Improving access to dermatological care in Prince Edward Island: A nurse practitioner-led initiative
Bibliographic record
Abstract
Skin diseases are common and occur throughout the lifespan. Researchers estimate that between\n30 and 70% of the world’s population live with a skin condition. Most dermatologic diseases are\nchronic in nature and decrease the daily quality of life. Untreated or poorly managed skin\nconditions pose a significant financial burden to the Canadian healthcare system. There is a\nshortage of dermatologists in Canada with no clear plan to address this shortage. Primary care\nproviders have identified that they feel underprepared to diagnose and treat many complex\ndermatologic conditions. Prince Edward Island (PEI) has one dermatologist to care for its\nexpanding population which has led to prolonged wait times. In this document, a nurse\npractitioner-led pilot project to increase accessibility to specialized dermatologic care is\nproposed. The pilot project will serve individuals of all ages and provide pharmacological and\nnonpharmacological interventions with regular follow-up to decrease the burden of skin\nconditions on this population. Nurse Practitioners (NP) possess the competencies to deliver\ncomprehensive dermatologic care and demonstrate increased/improved patient satisfaction,\nsymptom management, and quality of life. The proposed integration of an NP into existing\ndermatologic services on PEI will build capacity, improve wait times, and provide more ongoing\nsupport to those who suffer with dermatologic conditions on PEI.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".