Promoting Diversity, Equity, and Inclusion for Psoriatic Diseases
Bibliographic record
Abstract
There is increasing evidence of racial and ethnic disparities in the evaluation and treatment of people with psoriasis (PsO) and psoriatic arthritis, and inadequate racial/ethnic diversity in psoriatic disease (PsD) research. At the Group for Research and Assessment of Psoriasis and Psoriatic Arthritis (GRAPPA) 2021 annual meeting, a program focusing on diversity, equity, and inclusion (DEI) was presented to highlight known health and healthcare disparities in PsD. There is limited understanding of the prevalence and severity of PsD and how it affects quality of life among racial/ethnic minorities with PsD. Educational gaps and lack of diversity in our dermatology workforce may be contributing to challenges in appropriately diagnosing and treating PsO in darker skin types. Racial/ethnic minorities are also inadequately represented in clinical research, including trial recruitment and participation, for PsD. A panel of patient research partners, researchers, and clinicians ended the session with a broad discussion on how GRAPPA can better ensure racial/ethnic DEI in their educational, research, and clinical missions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.051 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.017 | 0.007 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.002 | 0.048 |
| Research integrity | 0.007 | 0.015 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".