What Defense Mechanisms Do Therapists Interpret In-Session?
Bibliographic record
Abstract
One of the key technical guidelines outlined by psychodynamic theorists and clinicians is for therapists to interpret a patient's most prominent defenses (Greenson, 1967; Langs, 1973). However, a debate exists about what constitutes a patient's most prominent defense and which defenses therapists actually choose to interpret in-session. This study aimed to shed light on this debate by examining 35 psychotherapy sessions (18 high alliance and 17 low alliance dyads) of individuals in therapy at a university counselling center. The analysis focused on comparing the patients' most prominent defenses and the range of defenses they utilized, and the therapists' most prominent interpretation level as well as the range of interpretation level. Paired sample t-tests showed no significant mean difference between sessions with low and high alliance scores in patient defense levels (e.g., frequency and range) and therapist interpretation levels (e.g., frequency and range). Significant differences were found between the range of patient defense levels and the range of therapist interpretation levels. Correlational analyses showed no significant relationship between patient defense levels and therapist interpretation levels on both the frequency and range levels. Clinical implications of these results, and directions for future research are discussed.
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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 | 0.008 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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.
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".