Training guidelines and competencies for serious mental illness (SMI) psychology.
Bibliographic record
Abstract
Individuals with serious mental illness (SMI) face unique and significant challenges that require evidence-based practices and clinicians who have advanced, comprehensive training to provide them. SMI affects about 5.5% of the U.S. population and results in serious health, social, and economic burdens. Despite advancements in treatment over the past 50 years, training programs for psychologists and other mental health providers have failed to keep up with these advances, underutilizing evidence-based assessments and interventions developed specifically for this population and found to be efficacious. To address this, the SMI Psychology Specialty has developed Training Guidelines to establish consistent, high-quality, and evidence-based training for postdoctoral psychologists. This article highlights selected features of the Training Guidelines for SMI Psychology. Although these were developed for postdoctoral training programs in SMI Psychology, they are applicable to training programs at all levels, and we hope that training programs in psychology and other mental health disciplines will incorporate these advances into their curricula. (PsycInfo Database Record (c) 2025 APA, all rights reserved).
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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 | 0.012 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.013 | 0.007 |
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".