Why Do Canadians Travel Abroad for Cosmetic Surgery? A Qualitative Analysis on Motivations for Cosmetic Surgery Tourism
Bibliographic record
Abstract
Background: Canadians are increasingly engaging in medial tourism. The purpose of this study was to review Canadians’ experiences with travelling abroad for cosmetic surgery, including primary motivations for seeking care outside of Canada. Methods: A qualitative analysis was conducted using semistructured interviews following a pre-determined topic guide. People who had undergone cosmetic surgery outside of Canada were interviewed. The interviews were transcribed and coded to determine motivational themes. Patients were recruited until thematic saturation was achieved. Results: Thematic saturation was achieved after recruitment of 11 patients. The most common motivational themes identified in this study for seeking cosmetic surgery outside of Canada included cost, post-operative care provided, marketing/customer service, and word-of-mouth. Member checking and theory triangulation were validation techniques used to verify identified themes. Mexico was the most common location for cosmetic tourism. The most common procedures were breast augmentation, mastopexy, and abdominoplasty. Participants gathered pre- and post-operative information primarily through pamphlets and contact with surgeons’ offices. Follow-up was only available for half of the participants in this study, and only 5 of the participants felt that they had received informed consent. Conclusions: The majority of participants engaged in cosmetic tourism due to cost reasons and the level of post-operative care provided.
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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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.018 | 0.009 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".