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Record W2970521046 · doi:10.1521/pdps.2019.47.3.257

Visiting Psychodynamic Scholars Enhance Psychodynamic Learning for Psychiatric Residents

2019· article· en· W2970521046 on OpenAlexaff
Debra A. Katz, Jennifer I. Downey

Bibliographic record

VenuePsychodynamic Psychiatry · 2019
Typearticle
Languageen
FieldPsychology
TopicPsychotherapy Techniques and Applications
Canadian institutionsColumbia College
Fundersnot available
KeywordsPsychodynamicsPsychodynamic psychotherapyCompetence (human resources)PsychologyPsychotherapistResidency trainingPsychological interventionPsychiatryMedicineMedical educationContinuing educationSocial psychology

Abstract

fetched live from OpenAlex

Limited resources in psychodynamic education in psychiatry residency training led the American Academy of Psychodynamic Psychiatry and Psychoanalysis to create The Victor J. Teichner Award. This award funds a psychodynamic scholar to visit a psychiatry residency program to teach residents and faculty over a 2−3-day period. Anonymous online surveys were distributed before and after the visit to 88 residents from three residency programs. In comparing pre-visit and post-visit groups, residents rated themselves as significantly improved in psychodynamic psychotherapy regarding (1) their level of competence (p < 0.005), (2) their ability to listen (p < 0.009), and (3) their ability to make interventions (p < 0.002). In addition, residents in psychodynamically underserved programs expressed strong interest in learning both general and psychodynamic psychotherapy skills despite being in programs they view as predominantly biologically oriented. These findings suggest that brief, intensive programs to enhance psychodynamic teaching are useful in psychiatric education and can result in a significant increase in residents' sense of competence in psychodynamic psychotherapy.

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), Insufficient payload (model declined to judge)
Consensus categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.209
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0010.002
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0020.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.003

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 head0.007
GPT teacher head0.341
Teacher spread0.334 · 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; both teacher heads agree on what is shown here.

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

Citations5
Published2019
Admission routes1
Has abstractyes

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