Exploring Informal Caregivers’ Roles in Medical Tourism through Qualitative Data Triangulation
Bibliographic record
Abstract
When Canadian medical tourists go abroad, they are often accompanied by friends and family, referred to as caregiver-companions, who provide informal care. These individuals play a role in patient decision-making and are stakeholders in medical tourism, yet little is known about their participation in this consumer health practice. To examine the roles that Canadian caregiver-companions play while accompanying medical tourists abroad, and to identify how multi-perspective qualitative data can augment our understanding of these roles, primary and secondary analysis was undertaken on datasets generated from multiple qualitative studies: semi-structured interviews with medical tourists, caregiver-companions, and international patient coordinators, and a survey with medical tourism facilitators. The findings from the triangulated analysis of these qualitative datasets serve to better understand the multiple, overlapping perspectives of different stakeholders in medical tourism. Results show that medical tourism caregivers act as companions, providing physical and emotional care; navigators, providing logistical assistance; and knowledge brokers, participating in decision-making and information exchange between medical tourists and professionals. Using data triangulation to examine the narratives of multiple stakeholders confirmed, altered, and augmented our knowledge of caregiver-companion roles. The unique perspectives offered by each participant group augment our understanding of caregiver roles and the practice of medical tourism.
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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 | 0.108 | 0.115 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.011 | 0.013 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.001 | 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".