Perception of Cosmetic Procedures among Middle Eastern Youth.
Bibliographic record
Abstract
BACKGROUND: In the past decade, there has been an increase in the number of cosmetic procedures performed globally. About one-third of individuals who undergo cosmetic procedures are under the age of 35. The United Arab Emirates (UAE) has become a regional hub for cosmetic procedures. This cross-sectional study examines the perception of cosmetic procedures among youth in the UAE. METHODS: A 63-question survey was electronically disseminated to university students to identify factors associated with the use of cosmetic procedures in this population. RESULTS: Ninety-one percent of the 178 participants were female, and 58 percent of them were aged 19 to 21. The majority of the participants felt cosmetic procedures are gaining acceptance in UAE society. Nearly 70 percent of participants felt that a legal and regulatory framework was important to determine the permissible age for undergoing cosmetic surgeries. LIMITATIONS: One limitation of the study lies in a modest response rate of 35.6 percent. There was a small number of male responders, and the assessment of differences between sex was not easy to conduct. CONCLUSION: Cosmetic procedures are increasingly being accepted among youth in the Middle East, with skin and nasal procedures being the most popular. The youth's concept of ideal body shape is in alignment with the Western ideas of beauty. Future research could characterize these perceptions in other cultures and explore differences in what is perceived to be beautiful in various parts of the world.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".