Results of a Formative Process Evaluation of Canada’s Student Mental Health Network (Preprint)
Bibliographic record
Abstract
UNSTRUCTURED Prevalence estimates for mental health issues among Canadian post-secondary students, including stress, psychological distress, and symptoms of common mental disorders continue to increase. In tandem, an increased acknowledgement of the need for comprehensive, upstream mental health promotion support for students has been highlighted. While the majority of post-secondary institutions offer some form of mental health promotion, research suggests that students are failing to access available supports due to notable barriers including lack of awareness of available resources, geographical or financial barriers, and/or lack of relevance and student interest in what is offered. Canada’s Student Mental Health Network (the Network) was created to fill these gaps, acting as a ‘one-stop shop’ for mental health education and evidence-based resources. This paper describes the results of a formative, process evaluation of the Network after approximately one year of operations. The goal was to assess acceptability and feasibility using a concurrent mixed methods evaluation design. Quantitative data collected through Google Analytics were used to assess website reach, engagement, and usership while qualitative, individual interviews provided more detailed insights into user experience and website attributes, as well as feedback on content delivery. Results provided supporting evidence for both acceptability and feasibility of the Network, in addition to identifying areas for additional content development and accessibility improvements moving forward.
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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 | 0.171 | 0.237 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".