Defense Mechanisms in Schizotypal, Borderline, Antisocial, and Narcissistic Personality Disorders
Bibliographic record
Abstract
Numerous authors have theorized that defense mechanisms play a role in personality disorders. We reviewed theoretical writings and empirical studies about defenses in schizotypal, borderline, antisocial, and narcissistic personality disorders, developing hypotheses about these differential relationships. We then examined these hypotheses using dynamic interview data rated for defenses in a study of participants (n = 107) diagnosed with these four personality disorder types. Overall, the prevalence of immature defenses was substantial, and all four disorders fit within the broad borderline personality organization construct. Defenses predicted the most variance in borderline and the least variance in schizotypal personality disorder, suggesting that dynamic factors played the largest role in borderline and the least in schizotypal personality. Central to borderline personality were strong associations with major image-distorting defenses, primarily splitting of self and other's images, and the hysterical level defenses, dissociation and repression. Narcissistic and antisocial personality disorders shared minor image-distorting defenses, such as omnipotence or devaluation, while narcissistic also used splitting of self-images and antisocial used disavowal defenses like denial. Overall, differential relationships between specific defenses and personality disorder types were largely consistent with the literature, and consistent with the importance that the treatment literature ascribes to working with defenses.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".