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Record W2947518429 · doi:10.1108/mhrj-06-2018-0016

Human givens rewind trauma treatment: description and conceptualisation

2019· article· en· W2947518429 on OpenAlexaff
Shona Adams, Steven Allan

Bibliographic record

VenueMental Health Review Journal · 2019
Typearticle
Languageen
FieldPsychology
TopicPosttraumatic Stress Disorder Research
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsShameSession (web analytics)Randomized controlled trialMedicinePsychotherapistPsychologyPhysical therapyComputer scienceSurgerySocial psychology

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.926
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0060.000

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.

Opus teacher head0.176
GPT teacher head0.469
Teacher spread0.293 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designOther design
Domainnot available
GenreEmpirical

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".

Quick stats

Citations4
Published2019
Admission routes1
Has abstractyes

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