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Record W1895982264 · doi:10.5539/ach.v8n1p100

Understanding Medical Tourists’ Perception of Private Hospital Service Quality in Penang Island

2015· article· en· W1895982264 on OpenAlexvenueno aff
Golnaz Nazem, Badaruddin Mohamed

Bibliographic record

VenueAsian Culture and History · 2015
Typearticle
Languageen
FieldHealth Professions
TopicGlobal Healthcare and Medical Tourism
Canadian institutionsnot available
FundersUniversiti Sains Malaysia
KeywordsTourismPerceptionMedical tourismService (business)Product (mathematics)Service qualityQuality (philosophy)MarketingNursingPsychologyBusinessAdvertisingFamily medicineMedicinePublic relationsGeographyPolitical science

Abstract

fetched live from OpenAlex

Medical tourism is one of the most successful types of tourism in Penang Island. Traveling beyond borders to obtain medical treatments is referred to as medical tourism. The purpose of this paper is to identify medical tourists’ perception of service quality based on the three elements of core product, physical environment, and interaction. Qualitative analysis is used; and twelve respondents are interviewed from a selected private hospital in Penang Island. The study found that there is an overall positive perception of medical tourism in Penang Island and X hospital with a 100% of the respondents willing to make the same choice of destination and hospital in the future for the purpose of medical tourism. Factors such as communication, staff, hospital facilities, and service influenced the positive perception. The results show that there are a few negative factors affecting medical tourists’ perception such as: lack of following established procedures among hospital staff, hospital building problems, and lack of information for international patients.

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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.035
Threshold uncertainty score0.071

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.183
GPT teacher head0.415
Teacher spread0.232 · 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 designObservational
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

Citations14
Published2015
Admission routes1
Has abstractyes

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