Body Image Distress and Its Associations From an International Sample of Men and Women Across the Adult Life Span: Web-Based Survey Study
Bibliographic record
Abstract
Background Previous research on body image distress mainly relied on samples that were small, generally homogeneous in age or sex, often limited to one geographical region, and were characterized by a lack of comprehensive analysis of multiple psychosocial domains. The research presented in this paper extends the international literature using the results of the web-based Global Health and Wellbeing Survey 2015. The survey included a large sample of both men and women aged ≥16 years from Australia, Canada, New Zealand, the United Kingdom, or the United States. Objective The main objectives of this study are to examine body image distress across the adult life span (≥16 years) and sex and assess the association between body image distress and various psychosocial risk and protective factors. Methods Data were extracted from the Global Health and Wellbeing Survey 2015, a web-based international self-report survey with 10,765 respondents, and compared with previous web-based surveys conducted in 2009 and 2012. Results The body image distress of young Australians (aged 16-25 years) significantly rose by 33% from 2009 to 2015. In 2015, 75.19% (961/1278) of 16- to 25-year-old adults reported body image distress worldwide, and a decline in body image distress was noted with increasing age. More women reported higher levels of body image distress than men (1953/3338, 58.51% vs 853/2175, 39.22%). Sex, age, current dieting status, perception of weight, psychological distress, alcohol and other substance misuse, and well-being significantly explained 24% of the variance in body image distress in a linear regression (F15,4966=105.8; P<.001). Conclusions This study demonstrates the significant interplay between body image distress and psychosocial factors across age and sex.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".