Integrating technology to increase the reach of CBT-I: state of the science and challenges ahead
Bibliographic record
Abstract
In this Round Table Discussion, an international panel of experts discuss issues related to the use of technology in the delivery of cognitive behavioral therapy for insomnia (CBT-I), in order to increase its reach. Panelists were, in alphabetical order, Carmela Alcántara, PhD, an Associate Professor at Columbia University School of Social Work in New York, USA, Bei Bei, PhD., an Associate Professor at Monash University in Melbourne, Australia, Charles M. Morin, PhD., a Professor of Psychology at Laval University in Quebec City, Canada, and Annemieke A. van Straten, PhD., a Professor of Clinical Psychology at the Vrije Universiteit in Amsterdam, the Netherlands. The session was chaired by Rachel Manber, PhD., a Professor of Psychiatry and Behavioral Sciences at Stanford University, in Palo Alto, California, USA. In their introductions each panelist discussed the use of technology in their respective country. All indicated that the most common way technology is used in the treatment of insomnia is through the use of video calls (telemedicine) to deliver individual CBT-I, and that this is mostly covered by publicly funded health insurance programs such as Medicare, especially since the COVID-19 pandemic. There are also some fully automated insomnia treatment programs, but they're often not covered by Medicare or other health insurance programs.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".