PROMOTING MENTAL HEALTH IN UNIVERSITY SETTINGS: A COMMUNITY-DRIVEN LIVING LAB APPROACH
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it