A phenomenological analysis of medical tourism: Investigating the lived experience of returning to Canada after osseointegration abroad
Bibliographic record
Abstract
BACKGROUND: Travel out of country for medical care is increasing for many reasons including costs, wait-lists, and availability of procedures. Until 2018, when the surgery was offered in only 1 province, Canadians with amputation wanting osseointegration (OI) had to travel out of country for the surgery. The purpose of this study was to understand the lived experience of accessing health care in Canada after having a procedure performed out of country. METHODS: This is a phenomenological study of persons with amputation who had OI outside of Canada. The grand tour interview question was "What was it like travelling to another country for OI surgery and then returning to access follow-up care in Canada?" RESULTS: There were 5 participants, and 5 themes emerged: (1) lack of support from Canadian physicians; (2) exceptional support from prosthetists and other members of the health care team; (3) continued reliance on the country where the procedure was initially performed; (4) self-advocacy for access to care; and (5) benefits of travelling outweighing the problems faced. CONCLUSIONS: These themes are not unique to OI, but to medical tourism as a whole. The lack of support was countered partly by a strong sense of self-advocacy from the participants and support from other members of the health care team.
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.025 | 0.022 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| 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".