Beyond Mental Health Maintenance: An Evaluation Framework Driven by Recovery-Focused Outcomes
Bibliographic record
Abstract
Evaluations of mental health outcomes have traditionally focused on indicators of mental health maintenance and stability. Although many jurisdictions in Canada are incorporating the principles of recovery into mental health services, evaluation practices and accountability frameworks have not kept pace with this shift in thinking. In our local regions of Waterloo and Wellington-Dufferin, Ontario, an innovative partnership between mental health agencies and consumer organizations has developed to apply principles of recovery to system-wide case management. In this article we describe the development of a comprehensive logic model framework designed to assess the impact of system-wide changes to practice. The model is grounded in the experiences, needs, and expectations of service users. This framework positions recovery-focused outcomes as alternatives to maintenance-based outcomes and as central to knowledge development and accountability in mental health systems.
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.
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 | 0.254 | 0.119 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.013 | 0.008 |
| Science and technology studies | 0.004 | 0.020 |
| Scholarly communication | 0.018 | 0.018 |
| Open science | 0.006 | 0.009 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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".