The effectiveness of Human Givens Rewind treatment for trauma
Bibliographic record
Abstract
Purpose Rewind is a trauma-focussed exposure technique that is part of Human Givens (HG) therapy. However, there have been no controlled studies examining the effectiveness or acceptability of Rewind, and a previous study comparing HG therapy outcomes with cognitive behaviour therapy (CBT) benchmarks has yet to be replicated. The paper aims to address these issues. Design/methodology/approach This preliminary investigation used an observational, quasi-experimental design. Using both between-subject and within-subject designs, the outcome measures of those who had Rewind in the second session and participants who had treatment-as-usual (TAU) in the second session followed by Rewind in the third session were compared. Pre–post treatment scores were used to evaluate the overall HG therapy and to compare with benchmarks. Findings Rewind was more effective than control treatment sessions, with 40 per cent recovered and 57 per cent having reliably improved or recovered after the Rewind treatment session. Rewind sessions were rated as acceptable as other treatment sessions. The effect size of HG therapy was above the CBT Clinical Outcomes in Routine Evaluation Outcome-10 (CORE-10) benchmark of 1.22. The recovery rate for treatment completers was 63 per cent, with 91 per cent recovered or reliably improved and was equivalent to the top quartile of services. Practical implications Rewind is a promising alternative trauma treatment, as people need not discuss details of the trauma, multiple traumas can be treated in one session and fewer treatment sessions may be needed. Originality/value There are few HG studies reported in the peer-reviewed literature. This preliminary study is the first controlled study of Rewind. The findings are also in line with previous research on HG therapy.
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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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.001 | 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".