Defensive functioning in individuals with depressive disorders: A systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: This systematic review and meta-analysis aimed to address the limited generalizability of studies on defense mechanisms in depression by comparing depressive individuals with non-clinical controls (aim a) and examining changes throughout psychological interventions (aim b) (PROSPERO CRD42023442620). METHODS: We followed PRISMA 2020 guidelines, searching PubMed/Web of Science/(EBSCO)PsycINFO until 13/04/2023 for studies evaluating defense mechanisms with measures based on the hierarchical model in depressive patients versus non-clinical controls or throughout psychological intervention. We conducted random-effect meta-analyses for mature defenses/non-mature (neurotic/immature) defenses/overall defensive functioning (ODF), with standardized mean difference (SMD) as outcome measure metric. Meta-regression/sub-group/sensitivity analyses were conducted. Study quality was appraised using the Newcastle-Ottawa Scale (NOS), and certainty of evidence for aim b outcomes was evaluated using GRADE (Grading of Recommendations, Assessment, Development and Evaluations). RESULTS: 18 studies were included (mean NOS score = 5.56). Depressive patients used significantly more non-mature defenses than non-clinical controls (SMD = 0.74; k = 13). Non-clinical controls did not significantly differ in use of mature defenses compared to depressive patients (SMD = 0.33; k = 14). Significant moderators were publication year/NOS score/geographical distribution/mean age for non-mature defenses and NOS score/geographical distribution for mature defenses. Throughout psychological interventions, only ODF significantly increased (SMD = 0.55; k = 2) (GRADE = very low). LIMITATIONS: Quality of many studies was medium/sub-optimal, and longitudinal studies were scarce. CONCLUSION: Individuals with depressive disorders show a high use of non-mature defenses that could be assessed and targeted in psychological interventions, especially in younger patients.
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How this classification was reachedexpand
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.004 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".