Promoting social responsibility amongst health care users: medical tourists’ perspectives on an information sheet regarding ethical concerns in medical tourism
Bibliographic record
Abstract
BACKGROUND: Medical tourists, persons that travel across international borders with the intention to access non-emergency medical care, may not be adequately informed of safety and ethical concerns related to the practice of medical tourism. Researchers indicate that the sources of information frequently used by medical tourists during their decision-making process may be biased and/or lack comprehensive information regarding individual safety and treatment outcomes, as well as potential impacts of the medical tourism industry on third parties. This paper explores the feedback from former Canadian medical tourists regarding the use of an information sheet to address this knowledge gap and raise awareness of the safety and ethical concerns related to medical tourism. RESULTS: According to feedback provided in interviews with former Canadian medical tourists, the majority of participants responded positively to the information sheet and indicated that this document prompted them to engage in further consideration of these issues. Participants indicated some frustration after reading the information sheet regarding a lack of know-how in terms of learning more about the concerns discussed in the document and changing their decision-making. This frustration was due to participants' desperation for medical care, a topic which participants frequently discussed regarding ethical concerns related to health care provision. CONCLUSIONS: The overall perceptions of former medical tourists indicate that an information sheet may promote further consideration of ethical concerns of medical tourism. However, given that these interviews were performed with former medical tourists, it remains unknown whether such a document might impact upon the decision-making of prospective medical tourists. Furthermore, participants indicated a need for an additional tool such as a website for continued discussion about these concerns. As such, along with dissemination of the information sheet, future research implications should include the development of a website for ongoing discussion that could contribute to a raised awareness of these concerns and potentially increase social responsibility in the medical tourism industry.
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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 | 0.019 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.012 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.006 |
| 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".