Bibliographic record
Abstract
Abstract Clinical depression is one of the most common psychiatric disorders treated by psychiatrists and psychotherapists. It also poses special problems to therapists as it is a complex disorder that affects the whole person – emotions, bodily functions, behaviours and thoughts. Although depression is treated successfully with antidepressant medication and psychotherapy, a significant number of depressives do not respond to either medication or existing psychotherapies. It is thus important for clinicians to continue to develop more effective treatments for depression. This article describes Cognitive Hypnotherapy (CH), an evidence‐based multimodal treatment for depression, which can be applied to a wide range of patients with depression. The components of CH are described in sufficient detail to allow for their replication and validation. Moreover, CH for depression provides a template for studying the additive effect of hypnosis as an adjunctive treatment with other medical and psychological disorders. Although this article emphasizes evidence‐based practice, this approach should not limit the scope of therapists' creativity in the application of hypnosis to the management of depression. Copyright © 2009 British Society of Experimental & Clinical Hypnosis. Published by John Wiley & Sons, Ltd.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| 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 | 0.001 |
| 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".