Investigating the Role of Upward Comparisons and Self-compassion on Stigma in People With Acne: Cross-sectional Study
Bibliographic record
Abstract
BACKGROUND: The use of image-laden social media is hypothesized as being implicated in psychological distress in individuals with conditions affecting their appearance. However, relatively little is known about the mechanisms involved in this relationship. OBJECTIVE: This cross-sectional study examined the relationship between photo-orientated social media use and feelings of stigmatization in adults with acne, and tested whether upward skin comparisons mediate and self-compassion moderates this relationship. METHODS: Adults (N=650) with acne symptoms completed web-based measures of social media use (daily Facebook or Instagram use, Facebook function use), self-compassion, skin appearance comparisons, and internalized stigmatization. RESULTS: Moderated-mediation and mediation analyses indicated that there was a significant indirect effect of Facebook photo use on stigmatization, mediated by upward appearance comparisons (estimation of indirect effect 11.03, SE 5.11, 95% CI 1.19-21.12). There was no significant relationship between Instagram use and feelings of stigmatization (estimation of indirect effect 0.0002, SE 0.005, 95% CI -0.011 to 0.009), yet upward appearance comparisons predicted feelings of stigmatization (B=0.99, P<.001). Self-compassion did not moderate the indirect or direct relationships between photo-orientated social media use and stigma. However, self-compassion was negatively correlated with upward appearance comparisons and feelings of stigmatization in both Facebook and Instagram users. CONCLUSIONS: The way that individuals engage with social media, and in particular make appearance comparisons, should be considered when working with individuals with skin-related distress. Interventions aimed at boosting self-compassion and reducing appearance comparisons may provide avenues for protecting against feelings of stigma.
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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 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".