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Record W2964834659 · doi:10.1177/0020731419866085

Medical Tourism in Guatemala: Qualitatively Exploring How Existing Health System Inequities Facilitate Sector Development

2019· article· en· W2964834659 on OpenAlexafffund
Alejandro Cerón, Valorie A. Crooks, Ronald Labonté, Jeremy Snyder, Walter Flores

Bibliographic record

VenueInternational Journal of Health Services · 2019
Typearticle
Languageen
FieldHealth Professions
TopicGlobal Healthcare and Medical Tourism
Canadian institutionsUniversity of OttawaOttawa Public HealthSimon Fraser University
FundersCanadian Institutes of Health Research
KeywordsMedical tourismHealth promotionEconomic growthTourismCommodificationPublic healthHealth equityThematic analysisHealth careHealth policyPrivate sectorSocial determinants of healthInternational healthBusinessPopulation healthEnvironmental healthPolitical scienceMedicineQualitative researchSociologyEconomicsNursingSocial science

Abstract

fetched live from OpenAlex

This article explores how existing health inequities in the Guatemalan health system facilitate the emergence of its medical tourism industry. We report on our thematic analysis of 50 key informant interviews conducted with 4 groups of stakeholders in the local medical tourism sector. Participants frequently discussed the interplay between the country’s longstanding health inequities and the promotion of medical tourism, characterized by 4 thematic viewpoints: the private health sector is already flourishing; the highly fragmented health system already faces multiple challenges; the underfunded public health sector has a weak regulatory capacity; and the commodification of health care has already advanced. Medical tourism and health inequities shape each other in low- and middle-income countries. In addition to the potential for medical tourism to exacerbate health inequities, previously existing health inequities create opportunities for the industry’s growth. Although regulation of the medical tourism industry is necessary, it needs to be implemented both at the domestic and supranational levels for it to be effective in preventing greater health inequities, and it needs to address the political and economic drivers that make health systems generate health disparities.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.010
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.283
Threshold uncertainty score0.984

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0100.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.248
GPT teacher head0.468
Teacher spread0.220 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations15
Published2019
Admission routes2
Has abstractyes

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