Overview of “Systematic Reviews” of the Built Environment’s Effects on Mental Health
Bibliographic record
Abstract
Good mental health is related to mental and psychological well-being, and there is growing interest in the potential role of the built environment on mental health, yet the evidence base underpinning the direct or indirect effects of the built environment is not fully clear. The aim of this overview is to assess the effect of the built environment on mental health-related outcomes. Methods . This study provides an overview of published systematic reviews (SRs) that assess the effect of the built environment on mental health. We reported the overview according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Databases searched until November 2019 included the Cochrane Database of Systematic Reviews, EMBASE, MEDLINE (OVID 1946 to present), LILACS, and PsycINFO. Two authors independently selected reviews, extracted data, and assessed the methodological quality of included reviews using the Assessing Methodological Quality of Systematic Reviews-2 (AMSTAR-2). Results . In total, 357 records were identified from a structured search of five databases combined with the references of the included studies, and eleven SRs were included in the narrative synthesis. Outcomes included mental health and well-being, depression and stress, and psychological distress. According to AMSTAR-2 scores, the quality assessment of the included SRs was categorized as “high” in two SRs and as “critically low” in nine SRs. According to the conclusions of the SRs reported by the authors, only one SR reported a “beneficial” effect on mental health and well-being outcomes. Conclusion . There was insufficient evidence to make firm conclusions on the effects of built environment interventions on mental health outcomes (well-being, depression and stress, and psychological distress). The evidence collected reported high heterogeneity (outcomes and measures) and a moderate- to low-quality assessment among the included SRs.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".