Use of photo filters is associated with muscle dysmorphia symptomatology among adolescents and young adults
Bibliographic record
Abstract
It has been documented in the literature that the use of photo filters to alter one's appearance may negatively impact body image and increase the risk for thinness-oriented disordered eating behaviors. However, the prior research has neglected to investigate the association between use of photo filters and muscle dysmorphia symptomatology, which was the aim of this study. Data from the Canadian Study of Adolescent Health Behaviors (2022; N = 912), a national study of adolescents and young adults in Canada, were analyzed. Linear regression analyses revealed that the use of photo filters was associated with greater muscle dysmorphia symptomatology, including total symptomatology and Appearance Intolerance, among the overall sample. Gender significantly moderated the association between photo filter use and muscle dysmorphia symptomatology, whereby boys and young men, compared to girls and young women, who reported photo filter use had greater Drive for Size and Functional Impairment symptomatology. Findings expand prior research by emphasizing that photo filter use is related to muscularity-oriented body image concerns and behaviors. Future research is needed to elucidate the mechanisms that underpin this association.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".