Medical Tourism: The Role of Communication Regarding Risks and Benefits of Obtaining Medical Services Abroad.
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it