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Record W4293123353 · doi:10.1521/jsyt.2022.41.1.33

An Experiment in Systemic Family Therapy Training: A Trainer and Trainee Perspective

2022· article· en· W4293123353 on OpenAlexvenueno aff
Harlene Anderson, Anne Hearon Rambo

Bibliographic record

VenueJournal of Systemic Therapies · 2022
Typearticle
Languageen
FieldPsychology
TopicCounseling, Therapy, and Family Dynamics
Canadian institutionsnot available
Fundersnot available
KeywordsTrainerSystemic therapyPerspective (graphical)Objectivity (philosophy)Family therapyPsychologyTraining (meteorology)PsychotherapistMedical educationMedicineComputer scienceEpistemologyArtificial intelligence

Abstract

fetched live from OpenAlex

This paper describes an experiment in systemic family therapy training that is part of an on-going attempt to relate the concepts and practices of systemic family therapy to systemic family therapy training. An example of a training framework, Systemic Case Consultation, is presented. Through intertwining trainer and trainee voices, we share two levels of observation: (1) our descriptions of the framework and (2) our descriptions of our experiences. Namely, the trainer simultaneously reviews the framework and comments on her experiences; and likewise the trainee simultaneously provides a clinical example and relates her experiences. The processes described are intricate and evolutionary, and thus, extremely difficult to present and analyze with objectivity. We realize that there are multiple operationalizations of systemic theory and present our work as only one.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.125
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.040
GPT teacher head0.320
Teacher spread0.280 · 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 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
Published2022
Admission routes1
Has abstractyes

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