Examining body appreciation in six countries: The impact of age and sociocultural pressure
Bibliographic record
Abstract
Previous research on body appreciation across the lifespan has produced conflicting results that it increases with age, decreases with age, or is generally stable with an increase in women over 50-years-old. Furthermore, most of the research has been conducted in White, Western populations. Cross-cultural research suggests that both Chinese and African women experience similar sociocultural pressures as White Western women, and that appearance ideals are shifting to resemble a more Western ideal. We cross-sectionally and cross-culturally examined body appreciation across the lifespan, recruiting White Western women (UK, USA, Canada, and Australia), Black Nigerian women, and Chinese women. 1186 women aged 18-80 completed measures of body appreciation, internalisation of thin and athletic ideals, and perceived sociocultural pressure. Body appreciation did not vary with age in women from any country. Nigerian women reported the highest body appreciation, and Western women the lowest. Higher thin/athletic ideal internalisation, and higher perceived sociocultural pressure were significantly associated with lower body appreciation in all countries and age-groups. Overall, our findings indicate that although levels of body appreciation differ drastically between ethnicities and cultures, it is generally stable across age, and shows cross-culturally robust relationships between sociocultural internalisation and pressure.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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) | 0.001 | 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".