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Record W2953034069 · doi:10.1177/1937586719855336

Layout Planning in Healthcare Facilities: A Systematic Review

2019· review· en· W2953034069 on OpenAlexaff
Guilherme Brittes Benitez, Giovani J.C. da Silveira, Flávio S. Fogliatto

Bibliographic record

VenueHERD Health Environments Research & Design Journal · 2019
Typereview
Languageen
FieldMedicine
TopicDigital Imaging in Medicine
Canadian institutionsUniversity of Calgary
FundersCoordenação de Aperfeiçoamento de Pessoal de Nível Superior
KeywordsVariety (cybernetics)Health careLimitingSystematic reviewComputer scienceSubject (documents)Management scienceProcess managementRisk analysis (engineering)MEDLINEBusinessEngineeringWorld Wide Web

Abstract

fetched live from OpenAlex

This study presents a systematic review of the literature on layout planning in healthcare facilities. The review includes 81 articles from journals, conferences, books, and other documents. Articles were classified in two groups according to their main contents including (i) concepts and guidelines and (ii) techniques and tools to assist in layout planning in healthcare facilities. Results indicate that a great variety of concepts and tools have been used to solve layout problems in healthcare. However, healthcare environments such as hospitals can be complex, limiting the ability to obtain optimal layout solutions. Influential factors may include the flows of patients, staff, materials, and information; layout planning and implementation costs; staff and patients safety and well-being; and environmental contamination, among others. The articles reviewed discussed and often proposed solutions covering one or more factors. Results helped us to propose future research directions on the subject.

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)0.013
metaresearch head score (Gemma)0.063
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.017
Threshold uncertainty score0.067

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0130.063
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0060.007
Bibliometrics0.0170.017
Science and technology studies0.0010.001
Scholarly communication0.0030.004
Open science0.0020.002
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0050.001

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 head0.483
GPT teacher head0.552
Teacher spread0.069 · 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 designSystematic review
Domainnot available
GenreReview

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

Citations42
Published2019
Admission routes1
Has abstractyes

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