Impact of the residential green space environment on the prevalence and mortality of Type 2 diabetes mellitus.
Bibliographic record
Abstract
OBJECTIVE: The residential green space environment plays a significant role in the progression of social, neuropsychological, behavioral, and public health. Green spaces are considered one of the most important components of healthy life events. This study investigated the impact of the green space environment on the prevalence and mortality of type 2 diabetes mellitus. MATERIALS AND METHODS: In this study, 110 research articles were initially identified through search engines (Web of Science, Pub-Med, Medline, EMBASE, Scopus) using the keywords "green space, environment, prevalence, mortality, diabetes mellitus." Finally, out of 110, 16 (14.54%) original research publications were included in the analysis, and the remaining 94 (85.45%) articles were excluded. The sample size of these 16 studies was 4,615,359. These studies originated from China (4), Canada (3), the United States of America (2), Australia (2), and one study each from the United Kingdom, Hong Kong, Korea, Belgium, and Bangladesh. The data on prevalence and diabetes mellitus were recorded and analyzed. RESULTS: Worldwide total of 16 studies met the selection criteria. The results showed that a high green space environment was significantly associated with a decreased prevalence of diabetes mellitus (13 studies; OR=0.875, 95% CI=0.859-0.891; p<0.001; I2=61.0%) and mortality (3 studies; HR=0.917, 95% CI=0.904-0.930; p<0.001; I2=75.4%). The findings support the hypothesis that a green space environment significantly reduces the prevalence and mortality of diabetes mellitus. CONCLUSIONS: The residential green space environment significantly decreases the prevalence and mortality of type 2 diabetes mellitus. It is suggested to establish strategies to keep residential areas and living environment green and clean to minimize air pollution and fight diabetes mellitus.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.006 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 source (direct Gemma or distilled Codex), 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".