MétaCan
Menu
Back to cohort
Record W2883404115 · doi:10.1186/s12992-018-0387-0

Medical tourism and national health care systems: an institutionalist research agenda

2018· article· en· W2883404115 on OpenAlexfundaboutno aff
Daniel Béland, Amy Zarzeczny

Bibliographic record

VenueGlobalization and Health · 2018
Typearticle
Languageen
FieldHealth Professions
TopicGlobal Healthcare and Medical Tourism
Canadian institutionsnot available
FundersCanada Research Chairs
KeywordsMedical tourismNexus (standard)ScholarshipTourismScope (computer science)Health services researchHealth policyHealth careSocial policyComparative researchPublic healthPolitical sciencePublic administrationPublic relationsEconomic growthSociologyMedicineEconomicsSocial scienceNursingLaw

Abstract

fetched live from OpenAlex

Although a growing body of literature has emerged to study medical tourism and address the policy challenges it creates for national health care systems, the comparative scholarship on the topic remains too limited in scope. In this article, we draw on the existing literature to discuss a comparative research agenda on medical tourism that stresses the multifaceted relationship between medical tourism and the institutional characteristics of national health care systems. On the one hand, we claim that such characteristics shape the demand for medical tourism in each country. On the other hand, the institutional characteristics of each national health care system can shape the very nature of the impact of medical tourism on that particular country. Using the examples of Canada and the United States, this article formulates a systematic institutionalist research agenda to explore these two related sides of the medical tourism-health care system nexus with a view to informing future policy work in this field.

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 imitation

Not 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.

metaresearch head score (Codex)0.008
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.016
Threshold uncertainty score0.101

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.005
Science and technology studies0.0050.035
Scholarly communication0.0120.012
Open science0.0020.008
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0050.000

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 head0.265
GPT teacher head0.584
Teacher spread0.320 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
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

Citations77
Published2018
Admission routes2
Has abstractyes

Explore more

Same venueGlobalization and HealthSame topicGlobal Healthcare and Medical TourismFrench-language works237,207