GD04 Patient education for all! Skin of colour under-represented again
Bibliographic record
Abstract
Abstract Disparities in healthcare provision have a detrimental impact on health outcomes, which have been suffered by many patient cohorts, whether they are defined socially, economically or by the colour of their skin (Buster KJ, Stevens EI, Elmets CA. Dermatologic health disparities. Dermatol Clin 2012; 30:53–9). In the field of dermatology, we rely hugely on visual presentations. As dermatologists, the visible manifestations of disease, and their evolution, are fundamental to how we formulate diagnoses and treatment strategies. Similarly, visual depiction is important for patients to garner a wider understanding of their condition. This study aimed to analyse skin colour representation in patient information resources for a number of inflammatory conditions, including acne, atopic dermatitis, hidradenitis suppurativa, psoriasis and rosacea. Resources from multiple dermatological bodies including the British Skin Foundation, Irish Skin Foundation, American Academy of Dermatology Association, Canadian Dermatology Association, Dermatology Society of South Africa and the Australasian College of Dermatology were reviewed. Hard-copy pamphlets published by the associations were analysed; where pamphlets were unavailable, websites pertaining to each inflammatory condition were included. Images were divided by Fitzpatrick skin type into two categories: Fitzpatrick skin types I–IV and Fitzpatrick skin types V/VI. There is no formal definition of skin of colour, and for the purposes of this study, we referred to Fitzpatrick skin types V/VI. In total, 115 images were reviewed. People with skin of colour were represented in 5% (n = 6) of the images. This stark over-representation of Fitzpatrick skin types I–IV highlights the lack of inclusivity in current patient education resources. Consequently, patients with skin of colour may feel overlooked. This may foster a poor rapport between patients and physicians and could lead to mistrust and reduced engagement with healthcare services. Dermatological communities must join the global effort to eliminate racial disparities. The first steps have already been taken by many, in publishing and medical education, including the British Journal of Dermatology, which aims to enhance resources to study, describe and improve care for people with skin of colour (Guckian J, Ingram JR, Rajan N, Linos E. Dermatology is finally talking about race. Br J Dermatol 2021; 185:875–6). Patient information resources are another facet of our practice that need focus and reform. We must improve approachability, inclusiveness and trust within our specialty and work towards the overarching aim of delivering healthcare that is racially just.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.316 | 0.051 |
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".