Human givens rewind trauma treatment: description and conceptualisation
Bibliographic record
Abstract
Purpose Human Givens (HG) Rewind technique is a graded trauma-focused exposure treatment for post-traumatic stress disorder and trauma. The purpose of this paper is threefold: first, to describe the technique; second, to provide an outline of its potential benefits; and third, to present some preliminary evidence. Design/methodology/approach This paper provides an overview of HG therapy and describes the stages of HG Rewind trauma treatment and its potential benefits. Similarities and differences between Rewind and other Cognitive Behavioural Therapy techniques are explored. Possible underlying mechanisms are discussed. Findings Preliminary evidence suggests that Rewind could be a promising trauma treatment technique and that HG therapy might be cost effective. The findings highlight the need for further research and a randomised controlled trial (RCT) on Rewind is warranted. Practical implications During the rewind technique, the trauma does not need to be discussed in detail, making treatment potentially more accessible for shame-based traumas. Multiple traumas may be treated in one session, making it possible for treatment to potentially be completed in fewer sessions. Social implications This UK-based treatment may be cost effective and make treatment more accessible for people who do not want to discuss details of their trauma. Originality/value This is the first description of HG Rewind in the peer-reviewed literature. Alternative explanations for mechanisms underlying this trauma treatment are also presented.
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 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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.012 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".