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Record W2727993899 · doi:10.1080/15228878.2017.1336104

Working Through Countertransference: Navigating Between Safety and Paranoia for a Client With Complex Trauma History and Borderline Personality Organization

2017· article· en· W2727993899 on OpenAlexaff
Eunjung Lee

Bibliographic record

VenuePsychoanalytic Social Work · 2017
Typearticle
Languageen
FieldPsychology
TopicPsychotherapy Techniques and Applications
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsCountertransferenceParanoiaPsychologyPsychotherapistPsychodynamicsUnconscious mindProjective identificationTransferencePsychicTherapeutic relationshipInterpersonal communicationPsychoanalysisSocial psychologyPsychoanalytic theoryMedicine

Abstract

fetched live from OpenAlex

The aftermath of complex trauma deeply impacts one's self-organization and interpersonal relationships, often resulting in clients who present to therapy with borderline characteristics and are typically labeled as difficult to treat. Further clinical complications with paranoid features may quickly place the therapist at a loss with respect to managing perceived and/or actual threats to client safety. Using psychodynamic theories, especially Kleinian understandings of psychosis and Winnicottian approaches to early disturbance and its impact on the emergence of self, this article provides a detailed case illustration that explores how a critical reflection of countertransference as “enactment,” “communication,” and “imagination” can help the therapist to understand the client's unconscious symbolic psychic struggles and to guide treatment selections in the therapy process.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Case report · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.008
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0090.011
Scholarly communication0.0050.005
Open science0.0020.007
Research integrity0.0040.005
Insufficient payload (model declined to judge)0.0020.001

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 head0.090
GPT teacher head0.382
Teacher spread0.292 · 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 source (direct Gemma or distilled Codex), not a consensus.

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

Citations2
Published2017
Admission routes1
Has abstractyes

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