Framework for Establishing a Sustainable Medical Facility: A Case Study of Medical Tourism in Jordan
Bibliographic record
Abstract
A significant number of studies have assessed the impact of medical tourism from economic, technological and social perspectives. Few studies, however, have explored the development of the medical tourism sector from a sustainability perspective. This research brings a sustainability lens to medical tourism by extending Hart and Milstein’s framework (2003) for creating sustainable business value to advance the development of sustainable medical tourism facilities. To inform the analysis, the study conducted nine semi-structured interviews with members of the Jordan Medical Tourism Network (JMTN). Interview results confirmed the primary factors that motivate medical tourists, and characteristics of a sustainable medical tourism facility. The research provides insights on how sustainability is a driver of medical tourists’ decisions and a core aspect to be managed. The study also provides direction to advance sustainable medical tourism facilities in Jordan with replicability in other jurisdictions. The research proposes a path for medical tourism facilities to play further roles in their contribution to sustainable development by introducing a framework that aims to integrate four business strategies for establishing sustainable value through the integration of stakeholders’ interests and environmental practices.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.013 | 0.005 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".