Investigating the effectiveness of incorporating a stepped care approach into electronically delivered CBT for depression
Bibliographic record
Abstract
Introduction Depression is a leading cause of disability, annually affecting up to 300 million people worldwide, yet fewer than one third of patients receive care. Cognitive behavioural therapy (CBT) is an effective treatment for depression, but there are barriers to access therapy. Electronic CBT (e-CBT) can address these barriers, but the digital format may reduce personalization and patient compliance. A balanced, hybrid model (i.e., combination of e-CBT & supervised care) could make therapy scalable and effective through a stepped-care model: a care model that begins treatment with the least resource intensive, yet effective, method while slowly ‘stepping up’ to intensive care based on patients’ needs. Objectives -To examine the efficacy of a stepped-care e-CBT model for depression through reduction in depressive symptoms. -To develop a decision-making process that can effectively allocate the appropriate level of care for each patient. Methods This is a single-blinded randomized controlled trial (RCT). Participants were randomized to either the e-CBT group (n = 53) or the e-CBT with stepped care group (n = 26). Both groups received a 12/13-weeks e-CBT program tailored to depression. The e-CBT program was provided through a secure online mental health clinic called the Online Psychotherapy Tool (OPTT). Participants read through the sessions and completed assignments related to each session. Each participant was designated a care provider who was a trained research assistant. Participants in the experimental group received extra interventions based on their standard questionnaire scores, and textual data. Results Figure 1: The average PHQ-9 (A), QLESQ (B), and QIDS (C) scores pre-, mid-, and post- treatment for the e-CBT only (n = 53) and stepped care groups (n = 26). * Depressive symptoms: PHQ-9 (Patient Health Questionnaire-9) & QIDS (Quick Inventory of Depressive Symptomatology) * Quality of Life Measure: QLESQ (Quality of Life Enjoyment and Satisfaction Questionnaire – Short Form) Image: Conclusions Stepped care model can be reliable and effective method of delivering targeted care to future patients. Using this approach, the amount of care each patient receives is tailored to their needs, allowing for more efficient usage of scarce resources. This would also lower the general cost of care for each patient. By understanding the therapeutic needs of each patient, we can use these results to develop objective interventions and efficient algorithms to triage individuals. This technique could scale up care capacity without sacrificing the quality of care for each patient. Disclosure of Interest N. Alavi Shareolder of: OPTT inc, M. Omrani Shareolder of: OPTT inc, J. Jagayat: None Declared, A. Shirazi: None Declared, A. Kumar: None Declared, A. Pannu: None Declared, Y. Shao: None Declared
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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 | 0.002 | 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".