“Do your homework…and then hope for the best”: the challenges that medical tourism poses to Canadian family physicians’ support of patients’ informed decision-making
Bibliographic record
Abstract
BACKGROUND: Medical tourism-the practice where patients travel internationally to privately access medical care-may limit patients' regular physicians' abilities to contribute to the informed decision-making process. We address this issue by examining ways in which Canadian family doctors' typical involvement in patients' informed decision-making is challenged when their patients engage in medical tourism. METHODS: Focus groups were held with family physicians practicing in British Columbia, Canada. After receiving ethics approval, letters of invitation were faxed to family physicians in six cities. 22 physicians agreed to participate and focus groups ranged from two to six participants. Questions explored participants' perceptions of and experiences with medical tourism. A coding scheme was created using inductive and deductive codes that captured issues central to analytic themes identified by the investigators. Extracts of the coded data that dealt with informed decision-making were shared among the investigators in order to identify themes. Four themes were identified, all of which dealt with the challenges that medical tourism poses to family physicians' abilities to support medical tourists' informed decision-making. Findings relevant to each theme were contrasted against the existing medical tourism literature so as to assist in understanding their significance. RESULTS: Four key challenges were identified: 1) confusion and tensions related to the regular domestic physician's role in decision-making; 2) tendency to shift responsibility related to healthcare outcomes onto the patient because of the regular domestic physician's reduced role in shared decision-making; 3) strains on the patient-physician relationship and corresponding concern around the responsibility of the foreign physician; and 4) regular domestic physicians' concerns that treatments sought abroad may not be based on the best available medical evidence on treatment efficacy. CONCLUSIONS: Medical tourism is creating new challenges for Canadian family physicians who now find themselves needing to carefully negotiate their roles and responsibilities in the informed decision-making process of their patients who decide to seek private treatment abroad as medical tourists. These physicians can and should be educated to enable their patients to look critically at the information available about medical tourism providers and to ask critical questions of patients deciding to access care abroad.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.081 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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; both teacher heads agree on what is shown here.
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".