Observatory on Student Mental Health in Higher Education in Quebec, Canada: Protocol for a Program Evaluation Designed to Generate Impacts (Preprint)
Bibliographic record
Abstract
BACKGROUND In line with the Healthy Universities and Health Promoting Universities principles, higher education institutions must be health-promoting environments in order to contribute to the mental health of their communities. Despite this recognition, studies have highlighted the precariousness of mental health among students in higher education. Research is needed to better understand student mental health issues and how to address them. Knowledge mobilization strategies must also be deployed to ensure that research findings have practical impacts on HEIs. OBJECTIVE The Observatory on Student Mental Health in Higher Education (OSMHHE) is a network of over 320 members and collaborators across Quebec (Canada) and elsewhere. Its mission: to develop, promote and disseminate knowledge to foster and maintain a culture that supports the mental health of students in Quebec’s higher education system. This article describes the OSMHHE’S research protocol, funded by the Fonds de recherche du Québec, designed to generate meaningful and measurable impacts on student mental health. METHODS This research protocol consists of three components: 1) establishing a portrait of students' mental health and its determinants; 2) identifying and evaluating a variety of mental health-related practices; 3) assessing the implementation and impacts of the Observatory as a knowledge mobilization infrastructure. The first component adopts a quantitative approach, while the other two employ a mixed-method approach. RESULTS This project is funded from February 2023 to February 2028. The first OSMHHE periodic provincial survey took place in November 2024. Data was collected from 77 higher education institutions and 32,790 students. The analysis is underway, and the final report will be released in November 2025. It will provide portraits of the mental health status of students in higher education in Quebec, in terms of both well-being and mental health difficulties and disorders. Identifying the determinants of mental health will enable us to better guide HEIs in implementing practices or measures to improve mental health. Moreover, around 20 student mental health practices are currently being evaluated. Analyses are underway, and evaluation reports will be published starting in 2026. These practices are evaluated in terms of their impacts, as well as the conditions under which they were implemented, sustained, and scaled up. Evaluating their impacts will help advance knowledge on the effectiveness of these practices for the mental health of students. It is also essential for identifying promising practices that can be scaled up. Finally, evaluating the implementation of the OSMHHE infrastructure in 2026 will help advance knowledge about the effects that these infrastructures can produce. CONCLUSIONS OSMHHE projects are expected to have applications that extend well beyond the traditional scientific outputs. We want these results to enhance students’ mental health and to foster a culture and communities that promote student mental health in higher education.
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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 | 0.136 | 0.128 |
| Meta-epidemiology (narrow) | 0.004 | 0.005 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.013 | 0.005 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.129 | 0.019 |
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".