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Record W4412201030 · doi:10.3390/buildings15142443

A Systematic Review of Architectural Atmosphere That Fosters Mindfulness Constructs

2025· review· en· W4412201030 on OpenAlexaff
Chaniporn Thampanichwat, Limpasilp Sirisakdi, Sippakorn Petsirasan, Duangkamon Wutisun, Sathirat Singkham, Tarid Wongvorachan, Prima Phaibulputhipong, Suphat Bunyarittikit, Rungroj Wongmahasiri

Bibliographic record

VenueBuildings · 2025
Typereview
Languageen
FieldPsychology
TopicMindfulness and Compassion Interventions
Canadian institutionsUniversity of Alberta
FundersKing Mongkut's Institute of Technology Ladkrabang
KeywordsAtmosphere (unit)MindfulnessPsychologyArchitectural engineeringEngineeringSystems engineeringComputer sciencePsychotherapistGeographyMeteorology

Abstract

fetched live from OpenAlex

This study explores how architectural atmosphere can foster mindfulness constructs in response to the growing mental health crisis. Mindfulness, known for improving mental health, reducing stress, and enhancing overall well-being, is increasingly recognized as a potential solution to mental health challenges. However, research on how architectural atmosphere supports mindfulness is limited. This study systematically reviews architectural atmosphere features that promote mindfulness constructs, which includes awareness, openness, attention, focus, connection, and calmness. A literature review was conducted using the Scopus database, following PRISMA guidelines for transparency. Fifty-three articles were selected, focusing on mindfulness features in architectural atmosphere: awareness (4), openness (1), attention (28), focus (5), and connection (15). No studies were found on architectural atmosphere fostering calmness. The findings suggest that architectural atmosphere plays a significant role in supporting mindfulness, but further empirical studies are needed to validate these results in real-world contexts.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
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 score0.293
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0050.002
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0080.000

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 head0.036
GPT teacher head0.360
Teacher spread0.324 · 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.

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

Citations1
Published2025
Admission routes1
Has abstractyes

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