Cognitive-Behavioral Therapy in Intensive Case Management: A Multimethod Quantitative-Qualitative Study
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it