Outbound medical tourism from Mongolia: a qualitative examination of proposed domestic health system and policy responses to this trend
Bibliographic record
Abstract
BACKGROUND: Medical tourism is the practice of traveling across international boundaries in order to access medical care. Residents of low-to-middle income countries with strained or inadequate health systems have long traveled to other countries in order to access procedures not available in their home countries and to take advantage of higher quality care elsewhere. In Mongolia, for example, residents are traveling to China, Japan, Thailand, South Korea, and other countries for care. As a result of this practice, there are concerns that travel abroad from Mongolia and other countries risks impoverishing patients and their families. METHODS: In this paper, we present findings from 15 interviews with Mongolian medical tourism stakeholders about the impacts of, causes of, and responses to outbound medical tourism. These findings were developed using a case study methodology that also relied on tours of health care facilities and informal discussions with citizens and other stakeholders during April, 2012. RESULTS: Based on these findings, health policy changes are needed to address the outflow of Mongolian medical tourists. Key areas for reform include increasing funding for the Mongolian health system and enhancing the efficient use of these funds, improving training opportunities and incentives for health workers, altering the local culture of care to be more supportive of patients, and addressing concerns of corruption and favouritism in the health system. CONCLUSIONS: While these findings are specific to the Mongolian health system, other low-to-middle income countries experiencing outbound medical tourism will benefit from consideration of how these findings apply to their own contexts. As medical tourism is increasing in visibility globally, continued research on its impacts and context-specific policy responses are needed.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| 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".