EDITORIAL FOR THE SPECIAL ISSUE: New Developments in Community Treatment, Interventions, and Services for Severe Mental Illness: Innovative Treatments and Services for People With Serious Mental Illness: Looking Back and Moving Ahead
Bibliographic record
Abstract
As an introduction to this special issue on New Developments in Community Treatment, Interventions, and Services for Serious Mental Illness, we, the editors of this special issue (TL & GS), sought the perspective of Kim T. Mueser to revisit the great changes that have taken place over the years. We felt this historical and critical view of the field would be a great way to understand how we got to where we are now, in terms of new treatments and services related to work, psychological symptoms, stigma, housing, and recovery described in this special issue. As such, this narrative review revisits important milestones in psychiatric rehabilitation for people with persistent, severe, or serious mental illness (i.e., SMI), beginning with its roots and early developments, and followed by extensive research leading to the current plethora of empirically validated interventions and programs. The influence of the recovery movement and the shift towards more person-centred care are considered for the contemporary practice of psychiatric rehabilitation. We also provide a critical analysis of the status of our field, including both major accomplishments and ongoing challenges, such as limited access to evidence-based rehabilitation practices, new technologies, and workforce issues. Hopeful future directions are proposed for addressing these and other challenges. We hope this introduction, as well as the articles chosen and peer reviewed for this special issue, will help you discover the ever-burgeoning field of recovery-oriented services and treatments for people with SMI.
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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 | 0.006 | 0.031 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.009 | 0.014 |
| Insufficient payload (model declined to judge) | 0.022 | 0.009 |
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".