Acceptance of Cosmetic Surgery A Comparative Analysis based on Gender, Religion and Geographical Locations
Bibliographic record
Abstract
Cosmetic surgery involves enhancing, maintaining, and repairing a person's physical appearance through surgical procedures. This study employed a quantitative, cross-sectional survey design to examine acceptance levels of cosmetic surgery based on gender, place of residence, and religion. The study hypothesizes that acceptance is higher internationally than in Pakistan, females exhibit greater acceptance than males, and individuals of Islamic faith have lower acceptance than those of other religions. The Acceptance of Cosmetic Surgery Scale (ACSS) is used to assess participant’s opinions. A total of 233 participants, aged 18 to 35, were recruited via convenience sampling through online surveys across Pakistan, the USA, the Middle East, Canada, Italy, and the UK. Data were analyzed using SPSS 25, employing t-tests to examine differences in acceptance based on demographics. Findings revealed significant differences in acceptance between individuals of Islamic faith and those of other religions (t = -13.514, p < 0.01) and between those residing in Pakistan and internationally (t = -2.656, p < 0.05). However, no significant gender-based difference was observed (t = 0.169, p > 0.05). These findings highlight cultural and religious influences on cosmetic surgery perceptions. Future research should explore additional variables and use qualitative approaches to gain deeper insights into the motivations and concerns surrounding cosmetic surgery.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".