Predicting CBT modality, treatment participation, and reliable improvements for individuals with anxiety and depression in a specialized mental health centre: a retrospective population-based cohort study
Bibliographic record
Abstract
BACKGROUND: Cognitive Behaviour Therapy (CBT) is one of the most successful therapeutic approaches for treating anxiety and depression. Clinical trials show that for some clients, internet-based CBT (eCBT) is as effective as other CBT delivery modes. However, the fidelity of these effects may be weakened in real-world settings where clients and providers have the freedom to choose a CBT delivery mode and switch treatments at any time. The purpose of this study is to measure the CBT attendance rate and identify client-level characteristics associated with delivery mode selection and having reliable and clinically significant improvement (RCSI) of treatment in each delivery mode in a real-world CBT outpatient program. METHODS: This is a retrospective cohort analysis of electronic medical records collected between May 1, 2019, and March 31, 2022, at Ontario Shores Centre for Mental Health Sciences. Regression models were used to investigate the impact of individual client characteristics on participation and achieving RCSI of different CBT delivery modes. RESULTS: Our data show a high attendance rate for two and more CBT sessions across all modalities (98% of electronic, 94% of group, 100% of individual, and 99% of mixed CBT). Individuals were more likely to enter mixed and group CBT modality if they were younger, reported being employed, and reported higher depression severity at the baseline. Among the four modalities of CBT delivery, group CBT clients were least likely to have RCSI. Of those who started sessions, clients were significantly more likely to experience RCSI on the Patient Health Questionnaire (PHQ)-9 and the Generalized Anxiety Disorder (GAD)-7 if they were employed, reported more severe symptoms at baseline, and were living in the most deprived neighborhoods. CONCLUSIONS: This study will contribute to the body of knowledge about the implementation and treatment planning of different CBT delivery modes in real-world settings. With the changing clinical environment, it is possible to advocate for the adoption of the eCBT intervention to improve therapy practices and achieve better treatment success. The findings can help guide future CBT program planning based on client socio-demographic characteristics, allowing the optimal therapy type to be targeted to the right client at the right time.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".