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Record W2093044824 · doi:10.1080/10503307.2012.745955

Therapist interventions using the Psychodynamic Interventions Rating Scale (PIRS) in dynamic therapy, psychoanalysis and CBT

2012· article· en· W2093044824 on OpenAlexaff
Elisabeth Banon, J. Christopher Perry, Trent Semeniuk, Michael Bond, Yves de Roten, Anne Grete Hersoug, Jean‐Nicolas Despland

Bibliographic record

VenuePsychotherapy Research · 2012
Typearticle
Languageen
FieldPsychology
TopicPsychotherapy Techniques and Applications
Canadian institutionsJewish General Hospital
FundersUniversity of Pennsylvania
KeywordsPsychological interventionPsychodynamicsPsychotherapistPsychodynamic psychotherapyPsychologyIntervention (counseling)Rating scaleClinical psychologyPsychiatryDevelopmental psychology

Abstract

fetched live from OpenAlex

One requirement for psychotherapy research is an accurate assessment of therapeutic interventions across studies. This study compared frequency and depth of therapist interventions from a dynamic perspective across four studies, conducted in four countries, including three treatment arms of psychodynamic psychotherapy, and one each of psychoanalysis and CBT. All studies used the Psychodynamic Intervention Rating Scales (PIRS) to identify 10 interventions from transcribed whole sessions early and later in treatment. The PIRS adequately categorized all interventions, except in CBT (only 91-93% categorized). As hypothesized, interpretations were present in all dynamic therapies and relatively absent in CBT. Proportions of interpretations increased over time. Defense interpretations were more common than transference interpretations, which were most prevalent in psychoanalysis. Depth of interpretations also increased over time. These data can serve as norms for measuring where on the supportive-interpretive continuum a dynamic treatment lies, as well as identify potentially mutative interventions for further process and outcome study.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.571
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.003
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.267
GPT teacher head0.564
Teacher spread0.298 · 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 designObservational
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

Citations27
Published2012
Admission routes1
Has abstractyes

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