A challenging entanglement: health care providers’ perspectives on caring for ill and injured tourists on Cozumel Island, Mexico
Bibliographic record
Abstract
PURPOSE: Despite established knowledge that tourists often fall ill or are injured abroad, little is known about their treatment. The intent of this study was to explore health care professionals' treatment provision experiences on Cozumel Island, Mexico. METHODS: 13 semi-structured interviews were undertaken with professionals across a number of health care vocations on Cozumel Island. Interviews were transcribed and thematically analysed to determine common challenges faced in the provision of treatment for transnational tourists. RESULTS: Three thematic challenges emerged from the data: human and physical resource deficiencies, medical (mis)perceptions held by patients and complexities surrounding remuneration of care. Health care providers employ unique strategies to mitigate these challenges. CONCLUSION: Although many of these challenges exist within other touristic and peripheral spaces, we suggest that the challenges experienced by Cozumel Island's health care professionals, and their mitigation strategies, exist as part of a complex entanglement between the island's health care sector and its dominant tourism landscape. We call on tangential tourism services to take a larger role in ensuring the ease of access to, and provision of quality health care services for tourists on Cozumel Island.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.007 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| 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".