BH03 Do patients with afro-textured hair who are experiencing hair loss feel understood by their dermatologists? A cross-sectional study
Bibliographic record
Abstract
Abstract Providing good-quality care for skin and hair conditions in patients with skin of colour is an important area of dermatology. This study aims to analyse patient perceptions of their dermatology experience with hair loss and caring for afro-textured hair. We also examined whether they were signposted to the British Association of Dermatologists patient information leaflet (BAD PIL) ‘Caring for Afro-textured hair’ [British Association of Dermatologists. Caring for Afro-textured hair. Available at: https://www.bad.org.uk/pils/caring-for-afro-textured-hair (last accessed 19 April 2024)]. Patients with afro-textured hair attending a consultation for hair loss between 2020 and 2023 were included. Patients consented to participating in an anonymous online survey. A Google form was emailed to participants between December 2023 and January 2024, with responses recorded. A link to the BAD PIL was attached to the survey. Seventeen patients responded. Of these, 59% did not receive a diagnosis prior to seeing a dermatologist. In total, 71% of patients did not fully understand the cause of their hair loss and commonly used social media (53%), online websites (41%, including 18% using the BAD website) and family and friends (35%) to seek information about hair loss. Social media (35%) and family and friends (29%) were the most common forms of seeking information regarding caring for their hair. After seeing a dermatologist, 13 patients (76%) received a diagnosis. Fourteen patients (82%) were offered a biopsy, which 12 underwent. Overall, 29% of patients believed their dermatologist understood their hair loss very well, 24% somewhat well, 35% not very well and 6% not at all. Most patients (63%) had the same understanding about their hair loss after their consultation, and half of patients (47%) did not gain any confidence about managing their hair loss. Patients are more likely to refer to the BAD website after seeing a dermatologist (35%). However, only five patients (29%) were signposted to the BAD PIL during their consultation. Overall, 41% of patients found the leaflet somewhat or very helpful. Patients expressed that their consultation was more beneficial when they were seen by a doctor with afro-textured hair, and were disappointed when signposted to leaflets that featured hair loss in non-afro-textured hair. Others feel saddened because there is ‘no cure’ and current management has not helped. This study highlights the challenges that patients with afro-textured hair face in the management of their alopecia. There appears to be a perception that dermatologists do not fully understand hair loss in afro-textured hair, unless they have such hair themselves. Further education to dermatology colleagues is imperative to improve patient care and satisfaction. The BAD PIL is a useful signpost, which can be more reliable than patients relying on social media for advice.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".