PROMOTING MENTAL HEALTH IN UNIVERSITY SETTINGS: A COMMUNITY-DRIVEN LIVING LAB APPROACH
Bibliographic record
Abstract
The COVID-19 pandemic has significantly impacted the mental health of university students and employees worldwide. In response to the increased prevalence of anxiety and depressive symptoms observed during this public health crisis, a Living Lab dedicated to promoting mental health within university settings was established in 2021. The main objectives of this Living Lab are to: (a) identify key mental health challenges within Quebec (Canada) university communities; (b) explore actions, initiatives, and resources that support community mental health; (c) implement new mental health promotion initiatives; and (d) evaluate their impact. This presentation, based on a Canadian case study, provides an overview of the Living Lab’s four years of activity, drawing on the results of three research projects conducted during this period. The first section, based on quantitative survey data collected from 2020 to 2022 (n=6000), identifies the main mental health challenges faced by Quebec university students and employees. The second section, grounded in a qualitative study (n=60), highlights the strengths and limitations of mental health support resources available in university environments. The third section focuses on a specific initiative, the ILUMIN Station, a wellness room implemented on our campus. Drawing on both quantitative and qualitative data collected from over 200 participants, this section explores the implementation process and assesses the initiative’s effectiveness. Building on the findings from these three studies, the presentation concludes with reflections and actionable recommendations for promoting mental health across university campuses. This document is a poster.
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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 | 0.015 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.022 | 0.011 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.008 | 0.021 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".