Psychometric Validation and Factor Structure of the Appearance-Related Social Media Consciousness Scale Among Emerging Adults
Bibliographic record
Abstract
The highly-visual nature of many social media platforms encourages appearance-related social media consciousness (ASMC), or the persistent awareness of how attractive one might look to a social media audience. The recently developed ASMC Scale, previously validated with high school-aged adolescents, provides a promising tool for systematically examining ASMC and associations with mental health symptoms. The current study examined the psychometric properties of the ASMC Scale among emerging adult men and women. Participants for Study 1 were 428 emerging adults (M age = 21.9) from five Anglophone, industrialized countries (U.S., U.K., Canada, Australia, New Zealand). Results from Study 1 indicate that the 13-item ASMC Scale has a unidimensional structure, strong internal consistency, and measurement invariance across gender and sexual orientation subgroups. Participants from Study 2 were 296 U.S. college students (M age = 18.5). Results from Study 2 demonstrated the convergent validity (i.e., associations with related offline appearance concerns and cognitions) and incremental validity (i.e., associations with depressive symptoms and disordered eating) of the ASMC Scale. Findings suggest the ASMC Scale can be reliably used to assess the extent of emerging adults’ awareness of their appearance on social media, aiding future research investigating emerging adults’ social media experiences and mental health.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 | 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".