“You’re dealing with an emotionally charged individual…”: an industry perspective on the challenges posed by medical tourists’ informal caregiver-companions
Bibliographic record
Abstract
BACKGROUND: Patients engage in medical tourism when they privately obtain a medical care abroad. Previous research shows that many medical tourists travel abroad with friends and family members who provide support and assistance. Meanwhile, very little is known about this important stakeholder group, referred to here as caregiver-companions. In this article we examine the challenges that can be posed by caregiver-companions and the overall practice of informal caregiving in medical tourism from an industry perspective. Specifically, we report on the findings of interviews conducted with international patient coordinators (IPCs) who work at destination facilities. IPCs come into regular contact with caregiver-companions in their professional positions and thus are ideally suited to comment on trends they have observed among this stakeholder group as well as the challenges they can pose to medical tourists, health workers, and facilities. METHODS: We conducted 20 semi-structured interviews with 21 IPCs from 16 different facilities across nine countries. Topics probed in the interviews included caregiver-companion roles, IPCs' and others' interaction with caregiver-companions, and potential health and safety risks posed to medical tourists and caregiver-companions. Thematic analysis of the verbatim transcripts was employed. RESULTS: Although most participants encouraged medical tourists to travel with a caregiver-companion, many challenges associated with caregiver-companions were identified. Three themes best characterize the challenges that emerged: (1) caregiver-companions require time, attention and resources; (2) caregiver-companions can disrupt the provision of quality care; and (3) caregiver-companions can be exposed to risks. IPCs pointed out that caregiver-companions may, for example, have a negative impact on the patient through cost of accompaniment or inadequate care provision. Caregiver-companions may also create unanticipated or extra work for IPCs, as additional clients and by ignoring established organizational rules, routines, and expectations. Furthermore, caregiver-companions may be susceptible to stresses and health and safety risks, which would further deteriorate their own abilities to offer the patient quality care. CONCLUSIONS: Although caregiver-companions can pose challenges to medical tourists, health workers, and medical tourism facilities, they can also assist in enhancing best care and offering meaningful support to medical tourists. If caregiver-companions are open to collaboration with IPCs, and particularly in the form of information sharing, then their experience abroad can be safer and less stressful for themselves and, by extension, for the accompanied patients and facility staff.
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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 | 0.008 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.017 | 0.014 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 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".