Psychosocial burden and the impact of illness perceptions and stigma on quality of life, anxiety and depression in alopecia areata: results from the Alopecia + Me study
Bibliographic record
Abstract
BACKGROUND: Alopecia areata (AA) can significantly impact patients' quality of life (QoL) and mental health, leading to increased levels of anxiety and depression. It is unclear whether this impact is more strongly associated with disease severity or patients' disease perception, and which patients are more likely to have a greater psychological burden. OBJECTIVES: To examine the psychosocial impact of AA, while focusing on illness perceptions and stigma, aiming to identify high-risk subgroups and key perceptions linked to worse QoL, anxiety and depression. METHODS: This was a UK cross-sectional online study. It comprised 596 patients with AA who self-reported disease severity and completed the Dermatology Life Quality Index (DLQI), EuroQol 5-Dimensional 5-Level (EQ-5D-5L), Hospital Anxiety and Depression Scale (HADS), Stigma Scale for Chronic Illnesses 8-Item (SSCI-8) and Brief Illness Perception Questionnaire (BIPQ). RESULTS: Patients with AA perceived their condition as chronic and life-impacting, with limited personal or treatment control, significant emotional effects and high concern. Many patients reported high levels of anxiety, depression, stigma and impaired QoL, all strongly associated with illness perceptions. Hierarchical regression analyses showed that illness perceptions and stigma explained a higher proportion of variance in QoL, anxiety and depression than disease severity. Cluster analysis identified two distinct patient groups based on illness perceptions, with different levels of QoL, anxiety, depression and stigma. CONCLUSIONS: AA has a severe psychosocial impact, more strongly linked to patients' illness perceptions and stigma than disease severity. The identification of two distinct patient profiles based on illness perceptions reveals differences in psychosocial burden, highlighting those at risk of worse outcomes and underscoring the value of evaluating illness perceptions along with stigma in clinical practice to improve patient outcomes.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".