Exploring Skin Tone Diversity in a Plastic Surgery Resident Education Curriculum
Bibliographic record
Abstract
Background: Gaps remain in surgical education regarding the representation of skin tone diversity. To improve equity and prevent misdiagnosis leading to worsened health outcomes, efforts must be made to ensure educational photographs are representative of the diverse patient populations plastic surgery residents will be treated in their future practices. Methods: Four study investigators examined 96 h of recorded lecture seminars from a Canadian plastic surgery resident education curriculum from May 2020 to December 2021. Using Fitzpatrick skin type to codify skin tone, photographic images were individually classified and compared. Program lecturers and residents were invited to participate in an online anonymized survey to explore related perceptions of the curricula. Results: A total of 1990 images were included for analysis. Of these, 83.2% were Fitzpatrick types I to III, 13.1% were Fitzpatrick types IV to V, and 3.7% were Fitzpatrick type VI. There was a statistically greater proportion of Fitzpatrick I to III compared to types IV to V ( P < .01), and type VI ( P < .01). Fleiss’ Kappa was calculated to be 0.896, representing near-perfect agreement. In the survey, 61% (14/22) of faculty respondents believe they include enough diversity in their photographs, however, 46% (4 of 9) of resident respondents would like to see more diversity in lecturers’ photographs. Conclusions: There is an underrepresentation of medium (Fitzpatrick types IV-V) and dark (Fitzpatrick VI) images in plastic surgery resident educational images. Providing a curriculum that represents diverse patient populations is crucial to enabling competency and equity of care, particularly in a highly visual field. Incorporating skin tone diversity into educational curricula should be a priority for all plastic surgery programs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".