Stigma and psychological health in psoriasis patients based on the dual-factor model of mental health: the chain mediating roles of social appearance anxiety and alexithymia
Bibliographic record
Abstract
Background: Patients with psoriasis also often experience stigma due to skin lesions, and this stigma further leads to severe psychological problems such as anxiety and depression. However, it is unclear how, and under what conditions, stigma relates to mental health. This study aimed to investigate the current status and interrelationships between stigma, social appearance anxiety, alexithymia, and mental health in patients with psoriasis. It also sought to identify the factors that influenced their mental health and to examine the mediating roles of social appearance anxiety and alexithymia in the relationship between stigma and psychological health. Method: From June to December 2023, patients with psoriasis were recruited from the outpatient department or ward of the dermatology department of a tertiary hospital in Guangzhou. Patients were assessed using the General Information Questionnaire, the Psoriasis Stigma Scale, the Social Appearance Anxiety Scale, the Toronto Alexithymia Scale, and the Satisfaction with Life Scale. Structural equation modeling (SEM) was conducted using Amos 24.0 to explore the relationships among the variables, and mediation effects were tested using SPSS 26.0. Results: A total of 317 psoriasis patients were recruited to participate in the survey. The total score of stigma of patients was (82.03 ± 1.52), which was at a moderate level. The total score of social appearance anxiety scale was (49.38 ± 1.00), which was at a high level. The total score of negative mental health of patients was (2.77 ± 0.14), which was at a low level. The total score of positive mental health of patients was (20.14 ± 0.36), which was at a medium level. The findings revealed that social appearance anxiety and alexithymia play significant chain mediating roles between stigma and negative mental health in patients with psoriasis, with an effect size of -0.031. Similarly, these factors also mediate the relationship between stigma and positive mental health, with an effect size of 0.056. Conclusion: Stigma in patients with psoriasis can directly impact their mental health and can also influence it indirectly through social appearance anxiety and alexithymia. Both social appearance anxiety and alexithymia serve as mediators in the relationship between stigma and mental health in these patients.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".