Developing an informational tool for ethical engagement in medical tourism
Bibliographic record
Abstract
BACKGROUND: Medical tourism, the practice of persons intentionally travelling across international boundaries to access medical care, has drawn increasing attention from researchers, particularly in relation to potential ethical concerns of this practice. Researchers have expressed concern for potential negative impacts to individual safety, public health within both countries of origin for medical tourists and destination countries, and global health equity. However, these ethical concerns are not discussed within the sources of information commonly provided to medical tourists, and as such, medical tourists may not be aware of these concerns when engaging in medical tourism. This paper describes the methodology utilized to develop an information sheet intended to be disseminated to Canadian medical tourists to encourage contemplation and further public discussion of the ethical concerns in medical tourism. METHODS: The methodology for developing the information sheet drew on an iterative process to consider stakeholder feedback on the content and use of the information sheet as it might inform prospective medical tourists' decision making. This methodology includes a literature review as well as formative research with Canadian public health professionals and former medical tourists. RESULTS: The final information sheet underwent numerous revisions throughout the formative research process according to feedback from medical tourism stakeholders. These revisions focused primarily on making the information sheet concise with points that encourage individuals considering travelling for medical tourism to do further research regarding their safety both within the destination country, while travelling, and once returning to Canada, and the potential impacts of their trip on third parties. This methodology may be replicated for the development of information sheets intending to communicate ethical concerns of other practices to providers or consumers of a certain service.
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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 | 0.090 | 0.191 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.008 | 0.004 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.012 | 0.004 |
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".