Coping tendencies play partial mediating role between social support and anxiety/depression among Chinese keloid patients
Bibliographic record
Abstract
Background: In addition to producing pain and itching, keloids can cause psychological symptoms, including anxiety and depression. We aimed to investigate the prevalence and underlying factors of anxiety/depression in Chinese keloid patients and to explore the mediating role between social support and anxiety/depression. Methods: A total of 202 self-reported questionnaires were collected from keloid patients, including the General Information Questionnaire, Vancouver Scar Assessment Scale (VSS), Patient and Observer Scar Assessment Scale (POSAS), Dermatological Problems Quality of Life Inventory (DLQI), Social Support Rating Scale (SSRS), Simple Coping Styles Scale (SCSQ), Self-Efficacy Scale (GSES) and the Hospital Anxiety and Depression Scale (HADS). Results: The mean scores for anxiety and depression were 9.82 ± 2.20 and 7.96 ± 2.70, respectively. Annual income (OR = 0.258), GSES score (OR = 0.2955), pain symptoms (OR = 1.281) and coping styles (OR:3.321) were significantly associated with HADS anxiety in keloid patients (P<0.05), whereas use of support (OR = 0.607) was significantly associated with HADS depression in keloid patients (p<0.05). The area under the curve (AUC) for the combined anxiety ROC for annual income, coping styles, pain symptoms and GSES scores was 0.835. PROCESS analysis concluded that coping tendencies partially mediated the relationship between social support and depression, with the mediating effect accounting for 38.58% of the total effect. Conclusion: Coping tendency in keloid patients played a partial mediating role between social support and depression. Future studies should further explore how training in coping tendencies can enhance the effectiveness of social support to more effectively prevent and reduce depressive symptoms.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".