Do defense mechanisms vary according to the psychiatric disorder?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
OBJECTIVE: The aim of this study was to evaluate the defense mechanisms used by depressive and anxious patients without comorbidities compared to those used by controls and to determine whether these patterns differ between diagnoses. METHOD: The sample was composed of 167 psychiatric patients and 36 controls that were evaluated using the Defense Style Questionnaire 40. All subjects were evaluated through a clinical interview, and each evaluation was confirmed through the application of the Mini International Neuropsychiatric Interview, a structured psychiatric interview. We used ANOVA and discriminant analysis to assess differences between groups. RESULTS: Neurotic defense mechanisms discriminated controls from all patients except those with social anxiety. Immature defense mechanisms differentiated controls from all patients, as well as distinguished depressive patients from panic disorder and obsessive disorder patients. The discriminant analysis indicated that depressive patients are characterized by projection, panic disorder patients by sublimation and obsessive-compulsive patients by acting out. CONCLUSIONS: Depressive and anxious patients differ from other individuals in their use of defense mechanisms, and each diagnosis has a particular pattern. These findings could lead to the development of specific psychotherapeutic interventions.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it