Tourism discourse and medical tourists’ motivations to travel
Bibliographic record
Abstract
Purpose – This paper aims to respond to a knowledge gap regarding the motivations of medical tourists, the term used to describe persons that travel across borders with the intention of accessing medical care. Commonly cited motivations for engaging in medical tourism are typically based on speculation and provide generalizations for what is a contextualized practice. This research paper aims to complicate the commonly discussed motivations of medical tourists to provide a richer understanding of these motivations and the various contexts in which medical tourists may choose to travel for medical care. Design/methodology/approach – Drawing on semi-structured interviews with 32 former Canadian medical tourists, this study uses the Iso-Ahola’s motivation theory to analyze tourists’ motivations. Quotations from participants were used to highlight core themes relevant to critical theories of tourism. Findings – Participants’ discussions illuminated motivations to travel related to personal and interpersonal seeking as well as personal and interpersonal escaping. These motivations demonstrate the appropriateness of applying critical theories of tourism to the medical tourism industry. Research limitations/implications – This research is limited in its ability to link various motivations with particular contexts such as medical procedure and personal demographics. However, this study demonstrates that the three commonly cited motivations of medical tourists might oversimplify this phenomenon. Originality/value – By providing new insight into medical tourists’ motivations, this paper expands the conversation about medical tourists’ decision-making and how this is informed by tourism discourse. This insight may contribute to improved guidance for medical tourism stakeholders for more ethical and safe practices.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.012 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".