The Architectural Design Strategies That Promote Attention to Foster Mindfulness: A Systematic Review, Content Analysis and Meta-Analysis
Bibliographic record
Abstract
Attention is a pivotal component and a central vehicle of mindfulness, a psychological factor improving mental health. Despite architecture’s potential to encourage attention and mindfulness, there is still a research gap. This study aimed to investigate architectural design strategies that promotes attention in order to foster mindfulness. The research was carried out in three primary stages. The first step entailed conducting a systematic review by searching publications related to architecture that promotes attention from Scopus in February 2024. After considering the suitability and accessibility, 32 articles were included. No studies were found to have investigated the field of enhancing mindfulness. The second step utilized content analysis to decode the selected articles using a framework developed from literature reviews. All three coders decoded the data independently, allowing the main researcher to compile it into the final dataset. Finally, the data underwent Python meta-analysis for word frequency and association. The result revealed certain qualities that help achieve attention through architecture. The architectural atmosphere is most effective when it features natural forms and spaces that evoke a sense of enclosure. The lighting should emphasize natural light and uniformity, whereas the sound designs primarily concern acoustics, ambient, and noises, with controlled weather emphasizing air aspects. The building should utilize natural materials and incorporate object elements; the facade and entrance are particularly crucial components. Moreover, the colors of brick and green and views encompassing gardens and vegetation are among the qualities mentioned. Based on the analysis, the material, view, and color features were most congruent with the biophilic design concept. All these factors are expected to foster mindfulness, thereby improving mental health.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".