Perceived Stigma and Mental Health Disorders Among Adults With Alopecia Areata Living in Japan
Bibliographic record
Abstract
Alopecia areata (AA) is a common disorder that causes hair loss and can significantly impact quality of life, which may be partially due to AA-related stigma. Examining the impact of AA on psychosocial health is important for understanding the burden experienced by patients with AA. The primary objective of this study was to examine mental health and sleep conditions, hair growth satisfaction, and AA-related stigma perceptions among individuals diagnosed with AA in Japan. The study used patients' self-reported data collected from the National Health and Wellness Survey conducted in Japan in 2023. Collected data included demographic characteristics and comorbidities; among those with a self-reported clinical diagnosis of AA, additional information on clinical characteristics, treatments, and perceived AA-related stigma was captured. Results were analyzed and stratified by self-assessed disease severity. Among the full sample (30 013 adults living in Japan), 471 respondents reported a clinical diagnosis of AA, including 347 mild cases, 100 moderate cases, and 24 severe cases. A diagnosed mental health disorder in the past year was reported by 57 respondents (12.1%), and 67 (14.2%) reported a diagnosed sleep condition in the past year. Less than half of respondents (47.4%) were satisfied with their current hair growth, and satisfaction decreased with increasing disease severity. Overall, 70.3% of respondents reported feelings of embarrassment, 55.0% felt that others judged them negatively, and 50.3% felt that others treated them negatively due to AA. A higher proportion of respondents with a severe case (54.2%) reported feeling embarrassed to have AA "very much so" compared with respondents who had mild (15.3%) or moderate (26.0%) cases. Perceived AA-associated stigma increased with disease severity. Overall, this study demonstrated the prevalence of AA-related disease stigma and mental health conditions among individuals with AA living in Japan, underscoring the importance of mental health support for patients with AA.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".