Exploring relationships between perfectionism and Instagram use on body image concerns and the use of body modification strategies in men
Bibliographic record
Abstract
Social media has been associated with a variety of negative psychological and physical consequences (Varsani & Diedrichs, 2017). The current study is used to extend previous research conducted by Bolt and Arpin-Cribbie (2018), which noted a significant relationship between trait perfectionism and Instagram (IG) use on body image concerns. Although research in this area has primarily focused on vulnerability factors in women, current literature findings are used to suggest that the prevalence of body image concerns in men has increased significantly in recent years. A sample of 232 men recruited from popular social networking sites (SNSs) and a northern Ontario university took part in a study that examined the relationship between IG use and perfectionism, and their associations with body image concerns and body modification strategies in men. Participants completed an online survey assessing perfectionism, IG use, body satisfaction, appearance orientation (AO), fitness orientation(FO), the drive for muscularity (DFM) and the use of appearance and performance enhancing substances (APESs). In general, high IG users who were also higher in trait perfectionism (i.e., socially prescribed perfectionism [SPP] and self-oriented perfectionism [SOP]) reported a greater DFM and were also more likely to endorse the use of APESs. Positive associations were noted between both facets of trait perfectionism and AO, whereas only SOP was positively associated with FO. Higher IG use was also positively associated with FO and AO. Overall, these findings can be used to suggest that men with elevated trait perfectionism are particularly vulnerable to experiencing a high DFM accompanied by the use of APESs when they engage in high IG use.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".