ACCESS Open Minds at the University of Alberta: Transforming student mental health services in a large Canadian post‐secondary educational institution
Bibliographic record
Abstract
AIM: Demands for mental health services in post-secondary institutions are increasing. This paper describes key features of a response to these needs: ACCESS Open Minds University of Alberta (ACCESS OM UA) is focused on improving mental health services for first-year students, as youth transition to university and adulthood. METHODS: The core transformation activities at ACCESS OM UA are described, including early case identification, rapid access, appropriate and timely connections to follow-up care and engagement of students and families/carers. In addition, we depict local experiences of transforming existing services around these objectives. RESULTS: The ACCESS OM UA Network has brought together staff with diverse backgrounds in order to address the unique needs of students. Together with the addition of ACCESS Clinicians these elements represent a systematic effort to support not just mental health, but the student as a whole. Key learnings include the importance of community mapping to developing networks and partnerships, and engaging stakeholders from design through to implementation for transformation to be sustainable. CONCLUSIONS: Service transformation grounded in principles of community-based research allows for incorporation of local knowledge, expertise and opportunities. This approach requires ample time to consult, develop rapport between staff and stakeholders across diverse units and develop processes in keeping with local opportunities and constraints. Ongoing efforts will continue to monitor changing student needs and to evaluate and adapt the transformations outlined in this paper to reflect those needs.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.019 | 0.005 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".