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Record W2014601835 · doi:10.1080/1536710x.2015.1014532

Standing Up to the Black Cloud: Case Example of Narrative Therapy in the Motor Vehicle Sector

2015· article· en· W2014601835 on OpenAlexaff
Michelle V. Gibson

Bibliographic record

VenueJournal of Social Work in Disability & Rehabilitation · 2015
Typearticle
Languageen
FieldPsychology
TopicCounseling, Therapy, and Family Dynamics
Canadian institutionsMcMaster University
Fundersnot available
KeywordsNarrativeNarrative therapyPsychologyOccupational therapyPsychotherapistConversationRehabilitationSocial workWork (physics)Political sciencePsychiatryEngineeringLawLinguisticsCommunication

Abstract

fetched live from OpenAlex

Using a case study, this article presents narrative therapy as an effective psychotherapy practice for work with victims of motor vehicle accidents. By troubling the standard cognitive behavioral approaches as deficit focused, narrative therapy is outlined as an approach that is focused on the skills and values present in the client's life; it is an approach that allows the client to take authorship over his or her concerns and enact change. This article is meant to be an example of narrative therapy's usefulness and open space for conversation about rehabilitation therapies that focus less on structure and more on strength.

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)0.006
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.233
Threshold uncertainty score0.386

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.058
GPT teacher head0.354
Teacher spread0.296 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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

Citations1
Published2015
Admission routes1
Has abstractyes

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