Psychosocial Problems Associated with Vitiligo: A Correlational Study on Vitiligo Patients
Bibliographic record
Abstract
Psychosocial problems influence the mental health as well as daily living of individuals. Vitiligo is one of those diseases which are strongly associated with psychosocial problems among its victim. Individuals with this disease are found to experience marriage delay, workplace discomfort, anxiety, depression, and remained to fail to cope with their skin condition. To determine psychosocial problems among vitiligo cases and to evaluate the role of psychotherapy in case management of vitiligo patients, this study employed a quantitative correlational study design. Data was collected from skin clinics in Islamabad from 15th December 2017 to 15th April 20018. A sample of 100 respondents including 60 cases and 40 controls was selected through convenient sampling technique. Questionnaires on self-esteem, depression, and Dermatology life quality index (DLQI) were used for data collection. Descriptive and inferential analysis was performed for data analysis. Pearson correlation between vitiligo and psychosocial problems was significant at the p-value 0.01 level (2-tailed). Study results showed that vitiligo has a significant relationship with depression, self-esteem, and impaired quality of life among cases. Regression analysis among Vitiligo and psychosocial problems was significant with a p-value < .001. Moderation regression analysis between vitiligo effects and psychotherapy was not found significant. Vitiligo has been found associated with depression, low self-esteem, and impaired quality of life. However, the proposed effect of psychotherapy has not been confirmed. Therefore, the study suggests further research to evaluate the role of psychotherapy to manage psychosocial problems of vitiligo cases. Keywords: Psychosocial, problems, associated, correlational study, vitiligo patients.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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 | 0.000 | 0.001 |
| 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".