Studying Change in Defensive Functioning in Psychotherapy Using the Defense Mechanism Rating Scales: Four Hypotheses, Four Cases
Bibliographic record
Abstract
Defense mechanisms are one of the original and the most durable theoretical contributions of psychoanalysis to dynamic psychology. Research has shown that there is a hierarchy of the general level of adaptation of defenses, divided into seven levels, which can be summarized as the level of overall defensive functioning (ODF). This chapter examines how the quantitative assessment of defense mechanisms can yield indicators of the progress and outcome in psychotherapy research. Four cases are presented with short- to long-term psychotherapy and one with very long follow-up. Each demonstrates how different aspects of defensive functioning change over different time periods and states (i.e., depressed vs. not depressed) exemplifying four hypotheses about how defenses change. The first is that as individuals change, they increase their overall level of defensive functioning, and at the same time, variability in defensive functioning tends to decrease, indicating increased resilience to stress. The second is that change in defense levels occurs in a stepwise fashion in which individuals trade off defenses lower on the hierarchy for those in the middle and only later developing those at the top of the hierarchy. The third is that individuals and groups have their own rates of change, which may vary across naturalistic and different treatment conditions, yet to be determined. Depressed states may be associated with initially large changes that then decelerate, whereas personality disorders (PDs) may have long initial periods of induction in the therapeutic process (“priming”), before change is initiated and becomes, more or less linear. Treatments that increase this rate of change are likely to be seen as more effective. Finally, in line with most of the research to date, as defensive functioning improves, symptoms will decrease and other aspects of functioning will improve. Although single cases do not prove a hypothesis, these cases offer some empirical support, while clearly demonstrating the value of research in this field. Furthermore, the identification of defenses in verbatim interviews and psychotherapy sessions permits the moment-to-moment analysis of the apparent effect of interventions on defensive functioning, also a topic worthy of further research.
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 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 | 0.016 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 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".