The Role of the Campus Outdoor Environment on University Student Mental Health
Bibliographic record
Abstract
Background and objective The mental health and wellness of university students has been a pressing concern in recent years in the US and is becoming an even larger issue due to the COVID-19 Pandemic. Numerous studies have supported the idea that the natural environment can have a positive impact on mental health, but only a few studies focus on the role of university outdoor campus environments on student’s mental health. The main purpose of this study is to investigate the correlations between university student mental health and their campus’s outdoor environment. Methods An online survey was designed and distributed to students at Michigan State University, USA. Students were asked questions about their overall mental well-being, as well as questions about their environmental perceptions, outdoor activity, views to nature through windows and safety concerns regarding their outdoor campus environment. Results The major findings indicate a significant difference in mental health scores for windows in living quarters, where students with living quarter windows had better mental health scores (MHS) than students without living quarter windows. This study also found a marginally significant difference in MHS for students with classroom windows. Other results of this study include a significant difference in MHS for students’ perception of safety on campus, outdoor work time, and perception of greenspace on campus. Conclusion Future campus planner, landscape architects, university planners, and student counselors will use this study to determine what kinds of outdoor spaces should be created and used to improve the well-being of students.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".