Demographic Data Associated With Digital Inequity Reported in Patient-to-Provider Teledermatology Studies in the United States From 2011 to 2021: Scoping Review
Bibliographic record
Abstract
Background Patient-to-provider teledermatology relies on a patient’s access to technology to ensure a successful visit. However, access to broadband internet and technology varies across populations in the United States—leading to the digital divide. While teledermatology has been recognized as a model to improve access, little is known about how often demographic data associated with digital inequity are captured in studies. Objective Given the expansion of teledermatology over the past decade, we sought to determine how often demographic data associated with digital inequity are reported in patient-to-provider teledermatology studies. Methods A scoping literature review search was conducted using the search term teledermatology in the following databases: PubMed, Embase, and the Cochrane Database of Systematic Reviews. All studies published between December 31, 2011, and December 31, 2021, that evaluated patient-to-provider teledermatology were eligible. Results In total, 1412 publications describing teledermatology were identified, of which 46 met the inclusion criteria. Race or ethnicity was the most frequently reported demographic characteristic (28/46, 61%). However, only 41% (19/46) of studies were representative of race or ethnicity, defined as including >20% nonwhite participants. Studies rarely reported the number of participants greater than 65 years of age (14/46, 30%), preferred language (9/46, 20%), income (6/46, 13%), highest level of education (5/46, 11%), or access to a device (4/46, 9%). Studies conducted after the onset of the COVID-19 pandemic were significantly more likely to report preferred language (9/25, 36% vs 0%; P=.002) and appeared more likely to report other demographic data associated with digital inequity, without reaching statistical significance (P>.05). Conclusions Demographic data associated with digital inequity are rarely reported in patient-to-provider teledermatology studies to date. These studies frequently lack adequate representation of racial and ethnic minorities. With increased calls for equitable representation in dermatology studies, future teledermatology studies can improve the reporting of race and ethnicity and consider demographic data associated with digital inequity as an important criterion in research design.
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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 | 0.028 | 0.160 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.007 |
| Bibliometrics | 0.038 | 0.036 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".