A process study of long-term treatment: comparing a successful and a less successful outcome
Bibliographic record
Abstract
This study examined therapist technique and patient change in emotional experiencing and defense mechanisms in the successful and unsuccessful long-term psychoanalytic treatments of two male patients. Two consecutive sessions every 6 months were analyzed for each patient. Therapist interventions, patient defense mechanisms, and patient emotional experiencing were assessed using the Psychodynamic Intervention Rating Scale, Defense Mechanism Rating Scale and the Experiencing Scale, respectively. Between and within-session analyses were conducted to determine the impact of the patient’s defensive functioning and experiencing on therapist interventions, and the effect of therapist interventions on those same two patient variables. Pearson’s Coefficient was utilized for between-session analysis; within-session analysis tracking moment-to-moment changes in patient and therapist functioning was performed using lag sequential analysis. Across therapy, therapist use of Interpretive Interventions was associated with increased emotional engagement and decreased defense maturity; the use of Supportive Interventions had the opposite effect. Within-session analysis revealed that use of Supportive Interventions when emotional engagement is low, followed by interpretative interventions, occurred in the successful case. Persistent use of Supportive Interventions in the context of low emotional engagement was observed in the unsuccessful case. This study suggests that failure to stimulate emotional engagement through interpretation can negatively affect therapeutic outcome.
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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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".