Cognitive-Behavioral Therapy in Intensive Case Management: A Multimethod Quantitative-Qualitative Study
Bibliographic record
Abstract
Cognitive-behavioral therapy (CBT) has been shown to improve clinical outcomes in schizophrenia and severe and persistent mental illness, but access to it remains limited. One potential way to improve access to CBT is to provide it through intensive case management (ICM) teams. A 90-week quality improvement study was designed to assess if CBT could be implemented in ICM teams. Self-selected ICM clinicians (N=8) implemented CBT with their patients (N=40). These clinicians attended weekly seminars (36 h total) and group supervision (1.5 h/wk). Patient outcomes for this group were compared with those of other clinicians who did not attend the seminars [treatment as usual (TAU) clinicians (N=4)] and their patient population (N=49). Prescore and postscore on the Clinical Global Impressions scale and a quality-of-life scale (Montreal Life Skill Survey) were analyzed for completers in both groups (Clinical Global Impressions scores were analyzed for 25 patients in the CBT group and 29 patients in the TAU group). Weekly session reports by clinicians in the CBT group measured CBT interventions, session focus, and satisfaction with CBT. Qualitative data were obtained from clinicians in the CBT group. After 90 weeks, patients in the CBT group had fewer negative symptoms compared with patients in the TAU group. Our qualitative data describe 2 trajectories of patients: those who improved with CBT and those who did not, and they suggest factors that may impact patient trajectories in CBT. This study suggests that CBT can be used effectively in ICM teams working with patients suffering from severe and persistent mental illness.
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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 | 0.021 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".