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Record W4394556113 · doi:10.6084/m9.figshare.11316041

Timmins (2020) Study 1 data, Human Givens Rewind treatment for posttrauamtic stress in a police force

2019· dataset· en· W4394556113 on OpenAlexaboutno aff
Jayne Timmins

Bibliographic record

VenueFigshare · 2019
Typedataset
Languageen
FieldHealth Professions
TopicOccupational Health and Performance
Canadian institutionsnot available
Fundersnot available
KeywordsPsychology

Abstract

fetched live from OpenAlex

The purpose of this preliminary study was to investigate the effectiveness of the Rewind treatment for post traumatic stress in a police force setting. Officers and staff in the police force were referred for treatment by the Occupational Health advisors, and all those who were referred were treated with no exclusion criteria. Any staff with a possible diagnosis for PTSD were first seen by the Forces Medical Advisor for a diagnosis before making a referral for Rewind treatment. All the clients treated using Rewind in a specific police force between 2009 and 2013 were included and gave informed consent to participate in this study prior to starting treatment. All those treated by the service completed the CORE-OM before and after treatment. Those with a diagnosis of PTSD or PTS were also given the IES-R before and after treatment. The Griffin and Tyrrell (2004) HG treatment was used with the Griffin and Tyrrell (2001) protocol for the Rewind technique.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.053
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0530.012

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 head0.364
GPT teacher head0.547
Teacher spread0.184 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreDataset

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

Citations2
Published2019
Admission routes1
Has abstractyes

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