Use Of Medical Tourism For Hip And Knee Surgery In Osteoarthritis: A Qualitative Examination Of Distinctive Attitudinal Characteristics Among Canadian Patients
Bibliographic record
Abstract
Background: Medical tourism is the term that describes patients' international travel with the intention of seeking medical treatment.Some medical tourists go abroad for orthopaedic surgeries, including hip and knee resurfacing and replacement.In this article we examine the findings of interviews with Canadian medical tourists who went abroad for such surgeries to determine what is distinctive about their attitudes when compared to existing qualitative research findings about patients' decision-making in and experiences of these same procedures in their home countries.Methods: Fourteen Canadian medical tourists participated in semi-structured phone interviews, all of whom had gone abroad for hip or knee surgery to treat osteoarthritis.Transcripts were coded and thematically analysed, which involved comparing emerging findings to those in the existing qualitative literature on hip and knee surgery.Results: Three distinctive attitudinal characteristics among participants were identified when interview themes were compared to findings in the existing qualitative research on hip and knee surgery in osteoarthritis.These attitudinal characteristics were that the medical tourists we spoke with were: (1) comfortable health-related decision-makers;(2) unwavering in their views about procedure necessity and urgency; and (3) firm in their desires to maintain active lives.Conclusions: Compared to other patients reported on in the existing qualitative hip and knee surgery literature, medical tourists are less likely to question their need for surgery and are particularly active in their pursuit of surgical intervention.They are also comfortable with taking control of health-related decisions.Future research is needed to identify motivators behind patients' pursuit of care abroad, determine if the attitudinal characteristics identified here hold true for other patient groups, and ascertain the impact of these attitudinal characteristics on surgical outcomes.Arthritis care providers can use the attitudinal characteristics identified here to better advise osteoarthritis patients who are considering seeking care abroad.
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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.011 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".