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Record W1903380348 · doi:10.5539/mas.v9n12p43

The Impact of the Design of Hospitals on Hospital Hoteling, Healing Process and Medical Tourism

2015· article· en· W1903380348 on OpenAlexvenueno aff
Arash Abinama, Masoud Jafari

Bibliographic record

VenueModern Applied Science · 2015
Typearticle
Languageen
FieldHealth Professions
TopicGlobal Healthcare and Medical Tourism
Canadian institutionsnot available
Fundersnot available
KeywordsLife expectancyTourismExpectancy theoryProcess (computing)PsychologyMedicineNursingComputer scienceSocial psychologyGeographyEnvironmental health

Abstract

fetched live from OpenAlex

<p class="zhengwen"><span lang="EN-GB">The design of hospitals is extremely important; any person may spend a part of his life in a hospital. Considering human interaction with the environment, psychological aspects of design and their impact on the moods of inpatients and their companions and medical team ergonomics and performance, the process of designing hospitals becomes more complex. The design of operational spaces in hospitals lack appropriate and functional properties in this regard. This paper considers the studies on the role of architecture and appropriate design on increasing the quality of the hospital hoteling, patients’ satisfaction, life expectancy and improved healing process, considering the design of spaces for inpatients’ companion and medical team. Furthermore, considering the potential and capacity of admitting foreign patients in Iran, the impact of these factors on medical tourism is also investigated. The objective of the present research is applied and is descriptive</span><span lang="EN-GB">/analytical that considered various aspects of the problem from different perspectives. The findings of the research show that implementing the above standards in designing hospitals leads to increased satisfaction, improvement in inpatient morale, speedy healing process, and more relaxed patients’ family and medical staff. In addition, the impacts of the above items will result in increased medical tourism.</span></p>

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)0.005
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.514
Threshold uncertainty score0.606

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
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 head0.058
GPT teacher head0.434
Teacher spread0.377 · 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 designOther design
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

Citations9
Published2015
Admission routes1
Has abstractyes

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