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Record W4399713314 · doi:10.21606/drs.2024.901

Workplace wellbeing and interior design: A systematic literature review

2024· article· en· W4399713314 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueProceedings of DRS · 2024
Typearticle
Languageen
FieldPsychology
TopicFacilities and Workplace Management
Canadian institutionsDalhousie University
Fundersnot available
KeywordsSystematic reviewAffect (linguistics)Applied psychologyWorkplace safetyWork (physics)PsychologyKnowledge managementComputer scienceEngineeringOccupational safety and healthMEDLINEMedicinePolitical science

Abstract

fetched live from OpenAlex

This paper offers a systematic review of the literature on workplace wellbeing and interior design, exploring the creation and evaluation of appealing environments that enhance employee wellbeing. This paper adopts a systematic approach to review using the guidelines of Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). Multiple databases were searched. The final review included 55 studies out of 472 that examined factors related to workplace wellbeing. The findings of this study suggest that background noise and open-plan workspaces negatively affect workplace wellbeing, while visual connections with plants and natural objects enhance it. This paper extends the current literature in two ways. Firstly, by highlighting key factors that impact workplace wellbeing. Secondly, it divides factors that contribute to workplace wellbeing into three categories: positives, negatives, and moderate impact factors. Design professionals and workplace managers can utilize this information to identify features that contribute most to the overall work environment.

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.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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: none
Teacher disagreement score0.878
Threshold uncertainty score0.480

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.014
GPT teacher head0.274
Teacher spread0.260 · 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