Cognitive-behavioral conjoint therapy for PTSD: Harnessing the healing power of relationships.
Bibliographic record
Abstract
Part I: Background and Overview of CBCT for PTSD. An Introduction to Cognitive-Behavioral Conjoint Therapy. Initial Assessment, Case Conceptualization, and Working with Complex Cases. Part II: CBCT for PTSD Treatment Manual. Phase 1. Rationale for Treatment and Education about PTSD and Relationships. Session 1. Introduction to Treatment. Session 2. Safety Building. Phase 2. Satisfaction Enhancement and Undermining Avoidance. Session 3. Listening and Approaching. Session 4. Sharing Thoughts and Feelings: Emphasis on Feelings. Session 5. Sharing Thoughts and Feelings: Emphasis on Thoughts. Session 6. Getting U.N.S.T.U.C.K. Session 7. Problem Solving to Shrink PTSD. Phase 3. Making Meaning of the Trauma(s) and End of Therapy. Session 8. Acceptance. Session 9. Blame. Session10. Trust. Session 11. Control. Session 12. Emotional Closeness. Session 13. Physical Closeness. Session 14. Posttraumatic Growth. Session 15. Review and Reinforcement of Treatment Gains.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.004 |
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 source (direct Gemma or distilled Codex), 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".