Supporting Post-Secondary Implementation of Recovery-Oriented Practice in a Stepped Care Model
Bibliographic record
Abstract
Student mental health has been a growing concern for higher education communities for many years. Campuses have been struggling to keep up with the increasing demand for services which has been complicated further by the COVID-19 pandemic. A Stepped Care model (SCM) developed at a Canadian university has been offering new ways of organizing mental health resources based on open access, student choice, and recovery principles. There are diverse definitions of recovery in the literature and are usually based on values such as empowerment, respect, and self-determination. SCMs have been shown to increase access to resources and reduce or eliminate waitlists for supports. A Canadian non-profit organization (SCCG) has been supporting the implementation of SCMs on campus communities in North America through training and consultation. The paradigm shift from the dominant biomedical model of health, which is expert-driven and focused on pathology, to recovery-oriented practices is complex. Currently, SCCG does not have a detailed vision of recovery-oriented practice in SCMs and limited resources to support its implementation. This problem of practice to be addressed is the lack of visioning and strategic planning for recovery-oriented practice in SCMs being implemented in post-secondary settings. Possible solutions including visioning, and resource and training development are explored. A change implementation plan is discussed along with monitoring and evaluation and communicating the change process. This plan offers practical solutions to support SCCG in moving toward a unified vision of recovery in SCM, and tools to support its implementation in post-secondary contexts.
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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 | 0.029 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.007 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.006 | 0.023 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".