A Cross-Sectional Survey of Population-Wide Wait Times for Patients Seeking Medical vs. Cosmetic Dermatologic Care
Bibliographic record
Abstract
BACKGROUND: Though previous work has examined some aspects of the dermatology workforce shortage and access to dermatologic care, little research has addressed the effect of rising interest in cosmetic procedures on access to medical dermatologic care. Our objective was to determine the wait times for Urgent and Non-Urgent medical dermatologic care and Cosmetic dermatology services at a population level and to examine whether wait times for medical care are affected by offering cosmetic services. METHODS: A population-wide survey of dermatology practices using simulated calls asking for the earliest appointment for a Non-Urgent, Urgent and Cosmetic service. RESULTS: Response rates were greater than 89% for all types of care. Wait times across all types of care were significantly different from each other (all P < 0.05). Cosmetic care was associated with the shortest wait times (3.0 weeks; Interquartile Range (IQR) = 0.4-3.4), followed by Urgent care (9.0 weeks; IQR = 2.1-12.9), then Non-Urgent Care (12.7 weeks; IQR = 4.4-16.4). Wait times for practices offering only Urgent care were not different from practices offering both Urgent and Cosmetic care (10.3 vs. 7.0 weeks). INTERPRETATION: Longer wait times and greater variation for Urgent and Non-Urgent dermatologic care and shorter wait times and less variation for Cosmetic care. Wait times were significantly longer in regions with lower dermatologist density. Provision of Cosmetic services did not increase wait times for Urgent care. These findings suggest an overall dermatology workforce shortage and a need for a more streamlined referral system for dermatologic 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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".