The manifestation of transference during early psychotherapy sessions: Exploring an alternate data source for therapist narratives in transference research
Bibliographic record
Abstract
Abstract Introduction: Previous transference studies have compared in‐session client narratives about significant others to in‐session client narratives about the therapist, limiting data to the information that clients are willing to share with the therapist. Method: The first three sessions of 30 therapies with high‐functioning individuals were examined using the Core Conflictual Relationship Theme (CCRT) method. Client narratives about others were drawn from the psychotherapy sessions and client narratives about the therapist were drawn from a Participant Critical Event (PCE) interview conducted after the third session of therapy. Results: Factor analyses of the CCRT components indicated several relational patterns: a complementary pattern of relating characterised by a devaluation of the therapist and idealisation of others; a concordant relational transfer where clients feel bad with both the therapist and others; and as clients experience control issues with significant others, they wish to adopt a submissive stance toward the therapist. The results suggest that the source of therapist narratives may influence the results of transference research.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.033 | 0.111 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".