Medical Tourism: The Role of Communication Regarding Risks and Benefits of Obtaining Medical Services Abroad.
Bibliographic record
Abstract
The ever-increasing globalization of healthcare has led to a greater number of consumers using the World Wide Web for the purpose of accessing health information and medical services that transcends international borders (Kangas, 2010; Lunt, Mannion, & Exworthy, 2012; MacReady, 2007; Snyder, Crooks, Adams, Kingsbury, & Johnston, 2011). When faced with the high cost of health care or limited treatment options in the United States, more and more Americans are looking to developing countries to obtain a variety of health-related services, including cosmetic surgery, dentistry, diagnostic testing, fertility treatment, and major surgeries such as heart valve operations and organ transplants (Dalstrom, 2012; Snyder et al., 2011; Sono, Herlihy, & Bicker, 2011). The number of people buying health-related products and accessing health information and medical services in developing countries via the Internet is increasing (Lunt, Hardey, & Mannion, 2010). According to Turner (2010), in the United States, popularization of medical tourism is related to social inequalities, loss of employer-provided health insurance, rising premiums for health insurance, limited public funding of health care, and lack of access to affordable health care. Turner (2010) also contends that the United States, due to its large and growing population of uninsured, under-lnsured, and people struggling to pay rising health insurance premiums, has become a leading target market for foreign medical facilities seeking international customers. In contrast to these motivators, patients from countries with less restricted health care, such as Canada and the United Kingdom, can choose to travel to foreign countries for immediate medical attention as an alternative to the long wait periods of nationalized health care systems (Boyle, 2008).
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 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".