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Record W4401093154 · doi:10.5771/9781793648655

Experiential Unity Theory and Model

2022· book· en· W4401093154 on OpenAlexaboutno aff
Alyson Quinn

Bibliographic record

VenueLexington Books · 2022
Typebook
Languageen
FieldPsychology
TopicTeam Dynamics and Performance
Canadian institutionsnot available
Fundersnot available
KeywordsExperiential learningPsychologyEpistemologyPhilosophyMathematics education

Abstract

fetched live from OpenAlex

The second edition of Experiential Unity Theory and Model: Treating Trauma in Therapy addresses the impact of trauma prevalent in the field of group therapy and in individual counseling and proposes Experiential Unity theory and model as an effective treatment option. It offers an integrative treatment approach that adheres to the principles of neuroscience and utilizes bottom-up processing, and a right-brain orientation to engage and process trauma. This theory and model has been offered in Individual therapy sessions as a treatment for depression, anxiety, addictions and other trauma- related distress and in Psychiatric Group therapy programs for over fourteen years. It has also been taught in university courses and delivered in workshops internationally, and at the Trauma informed Practice Institute, Vancouver, BC, Canada.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.010
Threshold uncertainty score0.034

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.011
Scholarly communication0.0040.006
Open science0.0010.003
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0100.003

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 head0.021
GPT teacher head0.291
Teacher spread0.271 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
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

Citations0
Published2022
Admission routes1
Has abstractyes

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