Sustainable Wellness Tourism in Indonesia (Case Study on Health Tourism Development at Hanara Wellbeing Center Bandung)
Bibliographic record
Abstract
Wellness tourism is currently developing and starting to be recognized as an important aspect of tourism. Not only in Bali, Bandung is developing itself into a destination for foreign tourists for wellness tourism. Wellness tourism, which focuses on activities and experiences aimed at enhancing one's health and well-being, is intricately linked with sustainable tourism principles. This phenomenon is then studied using constructivist paradigms, qualitative methods, case study approaches, and social construction theory. Data collection techniques were participant observation for a year, interviews with 6 key informants and triangulation of 19 sources, literature study, and document study. The purpose of this study is to determine the development of wellness tourism in Bandung, Indonesia. The results showed that Bandung is an alternative to wellness tourism, because it was visited by patients from Malaysia, Canada, Singapore, Switzerland, the Philippines, Pakistan, Timor Leste and Australia. The uniqueness is: 1) Offering holistic health care; 2) Organizing complementary and alternative medicine under the supervision of doctors; 3) Spiritual healing-based care; 4) Teaching patients self-healing methods; 5) Not using chemical drugs; 6) Leaving the paternalistic model ;7) loyal patients are fostered in a community; and 8) using celebrities in promotions. Keywords: health, tourism, wellness
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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 | 0.000 | 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 | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".