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Record W4403277215 · doi:10.1080/03155986.2024.2411880

Control strategies for medical tourism development in public hospitals considering waiting time and tourism attraction

2024· article· en· W4403277215 on OpenAlexvenueno aff
Wuhua Chen, Xiaoling Yin

Bibliographic record

VenueINFOR Information Systems and Operational Research · 2024
Typearticle
Languageen
FieldHealth Professions
TopicGlobal Healthcare and Medical Tourism
Canadian institutionsnot available
FundersNational Natural Science Foundation of China
KeywordsAttractionTourismTourist attractionControl (management)Medical tourismMarketingBusinessAdvertisingGeographyEconomicsManagement

Abstract

fetched live from OpenAlex

Medical tourism has become one of the fastest-growing emerging industries in the world. For some countries where public hospitals are dominant, as serving foreign customers may increase hospital congestion, whether public hospitals are allowed to develop medical tourism has become a significant issue. Thus, based on queueing and game theories, this paper explores the control strategies for the development of medical tourism in public hospitals in terms of social welfare. In a market that includes government, public hospitals, domestic and foreign patients, short-term and long-term cases where the public system (i.e. public hospitals) will invest the incomes from foreign patients into the capacity expansion or not are investigated respectively. The results show that if government’s total budget, profitability of the tourism industry, or tourism attraction (or patients’ delay sensitivity) is high enough (or low enough), the social planner should allow the public hospitals to participate in medical tourism projects and not otherwise. Furthermore, we find that under certain conditions, public hospitals engaging in medical tourism projects may compromise the welfare of domestic patients or increase the government’s budget expenditure on the public hospital system.

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)0.007
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.750
Threshold uncertainty score0.680

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.002
Open science0.0000.000
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 head0.123
GPT teacher head0.464
Teacher spread0.341 · 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 designNot applicable
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

Citations0
Published2024
Admission routes1
Has abstractyes

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