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Record W3098314857 · doi:10.1080/02650533.2020.1835846

Make every session count for clients! Rethinking clinical social work practice from Single Session Therapy (SST): A case illustration of Emotion-Focused Therapy (EFT)

2020· article· en· W3098314857 on OpenAlexaff
Eunjung Lee, Marley Tratner

Bibliographic record

VenueJournal of Social Work Practice · 2020
Typearticle
Languageen
FieldPsychology
TopicCounseling, Therapy, and Family Dynamics
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsSession (web analytics)AllianceMental healthPsychologySocial workPsychotherapistIntervention (counseling)Single-subject designParadigm shiftService delivery frameworkApplied psychologyMedical educationService (business)MedicineComputer sciencePsychiatryBusinessPolitical science

Abstract

fetched live from OpenAlex

Re-thinking a service delivery paradigm within a single session therapy (SST) framework inevitably changes the ways we consider the therapeutic process – making each session count! In clinical practice, this means (1) the rapid development of therapeutic alliance and (2) the life of the therapy process, from assessment to intervention to evaluation condensed into a single session. To illustrate, this article analyzes the fully transcribed one of the master tapes in Emotion-Focused Therapy where chair work is demonstrated in a single session, and finds six processes in SST: (1) history taking; (2) formulation; (3) contracting; (4) working-through change; (5) evaluation; and (6) preparing for exit. A micro-analysis of the single session details how the therapist manages multiple tasks in SST. This analysis helps social workers to re-think service delivery and attrition rates in community mental health settings to enhance health equity and the provision of socially just mental health services.

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)0.005
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.669
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0010.002
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 head0.121
GPT teacher head0.404
Teacher spread0.284 · 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 designNot applicable
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

Citations7
Published2020
Admission routes1
Has abstractyes

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