Experiences and attitudes toward aesthetic procedures in East Asia: a cross-sectional survey of five geographical regions
Bibliographic record
Abstract
BACKGROUND: The demand for aesthetic procedures continues to grow globally, particularly in East Asian countries. The popularity of specific aesthetic procedures varies, however, depending on the particular East Asian geographical region being studied. This study aimed to evaluate the experiences of and attitudes toward aesthetic procedures in five East Asian countries/regions, including China, Japan, South Korea, Hong Kong, and Taiwan. METHODS: To recruit participants, an online questionnaire was designed and distributed on social media networks between May 2015 and March 2016. The statistical analysis was conducted using SPSS software, version 22.0. RESULTS: A total of 3,088 people responded (approximately 600 in each country/region). Of these, 940 participants (47.8%) responded that they had experienced at least one aesthetic procedure in the past. Taiwan had the highest number of participants who had experienced at least one procedure (264/940, 41%), with primarily non-surgical experiences. Only in South Korea did surgical cosmetic experiences exceed non-surgical cosmetic experiences (55.9% vs. 44.1%). The popularity of particular procedures and the motivation for undergoing aesthetic procedures varied by country. CONCLUSIONS: The popularity of aesthetic procedures continues to evolve. Similar trends were observed across the East Asian regions; however, each country had its unique demands and preferences. The information provided by this study can help aesthetic plastic surgeons further understand the patients in their corresponding region, customize their practice, and develop the requisite skills.
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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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".