They Go the Extra Mile, the Extra Ten Miles...”: Examining Canadian Medical Yourists’ Interactions with Health Care Workers Abroad
Bibliographic record
Abstract
Developing an understanding of medical tourists' interactions with their health care workers while abroad is important for a number of reasons. Social support has been linked to improved health outcomes for patients (Berkman et al., 2000; Lee and Rotheram-Borus, 2001; Uchino, 2004, 2006), while a lack of social support has been found to lead to higher mortality rates (Brummett et al., 2001; Rutledge et al., 2004). While abroad, medical tourists are not in a position to draw on their usual social support networks as they are away from home. It could be the case that workers in medical tourism facilities are aware of this and work to form a supportive and trusting bond with the patients given that they are away from home and unable to draw on their usual support networks. Furthermore, when patients perceive their relationship with their health care workers as positive, they have been shown to have a higher chance of improved health outcomes (Stewart et al., 2000; Arora, 2003; Beach et al., 2006; Street et al., 2009). There is no reason to think this would be any different for medical tourists. The patient-health care worker relationship can have important implications for patient health and therefore we believe that research into this topic using medical tourists' own experiential accounts can help to identify strategies that can be used to secure and improve this relationship.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.017 | 0.005 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".