Recovery and Severe Mental Illness: Description and Analysis
Bibliographic record
Abstract
The notion of recovery has been embraced by key stakeholders across Canada and elsewhere. This has led to a proliferation of definitions, models, and research on recovery, making it vitally important to examine the data to disentangle the evidence from the rhetoric. In this paper, first we ask, what do people living with severe mental illness (SMI) say about recovery in autobiographical accounts? Second, what do they say about recovery in qualitative studies? Third, from what we have uncovered about recovery, can we learn anything from quantitative studies about proportions of people leading lives of recovery? Finally, can we identify interventions and approaches that may be consistent or inconsistent with the grounded notions of recovery unearthed in this paper? We found that people with mental illness frequently state that recovery is a journey, characterized by a growing sense of agency and autonomy, as well as greater participation in normative activities, such as employment, education, and community life. However, the evidence suggests that most people with SMI still live in a manner inconsistent with recovery; for example, their unemployment rate is over 80%, and they are disproportionately vulnerable to homelessness, stigma, and victimization. Research stemming from rehabilitation science suggests that recovery can be enhanced by various evidence-based services, such as supported employment, as well as by clinical approaches, such as shared decision making and peer support. But these are not routinely available. As such, significant systemic changes are necessary to truly create a recovery-oriented mental health system.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.006 | 0.011 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".