Students as Researchers: An Inquiry into University Courtyards as Diverse and Inclusive Areas for Social Connection and Wellbeing
Bibliographic record
Abstract
Abstract Background Urban forests enhance mental health by reducing loneliness, fostering connections to nature, and reducing stress and anxiety. There is growing interest in understanding how urban forests can help support mental health across the life course, including among young adults. Given the known psychological and social benefits of nature-rich environments, it is critical to evaluate the functionality and usage of urban forest spaces for specific groups, particularly those at higher risk of mental health conditions like the members of this age group. Methods This student-led research study at the University of British Columbia’s Vancouver campus applied a mixed methods approach to assess the role of campus courtyards in supporting student wellbeing, with the ultimate aim of informing inclusive and effective spatial planning. Eight courtyards were analyzed via surveys and participant observation to understand their restorative and social benefits. Involving students as researchers played a vital role in offering alternative perspectives that helped identify previously overlooked gaps in this field. Results Our findings highlight the value of nearby, convenient greenspaces for young adults. There were 46 survey participants who shared their experiences in UBC courtyards, focusing on restorative and social benefits; 139 courtyard uses were observed by student researchers. Courtyards varied in biodiversity, order, and seclusion. Biodiverse courtyards received higher ratings for restoration, while social courtyards were linked to less reported guilt due to taking breaks. Across courtyard design typologies, students valued privacy, vegetation, and a sense of inclusion, although feelings of loneliness and discontent persisted. Conclusions This study demonstrates the value in engaging students as researchers to understand student perceptions of a campus urban forest for supporting wellbeing, social connection, and academic achievement. Although greenspaces such as courtyards are known to have restorative potential, they are not always designed to fully support student needs, highlighting the importance of student-informed planning frameworks that address existing gaps and foster more accessible, functional, and representative greenspaces on campuses.
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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 | 0.016 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.020 | 0.018 |
| Scholarly communication | 0.015 | 0.008 |
| Open science | 0.003 | 0.016 |
| Research integrity | 0.003 | 0.005 |
| 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".