Defensive Functioning in Patients with Depressive Disorders: a Study Protocol for a Systematic Review and Meta-Analysis
Bibliographic record
Abstract
SCOPO DEL LAVORO: The role of defensive functioning in understanding Depressive Disorders is supported \n(Calati et al., 2010); however, there is no updated comprehensive overview. In order to fill this gap, we have developed a Study Protocol for a Systematic Review and Meta-Analysis with the following research questions: (a) Do mature defenses, non-mature defenses, and Overall Defensive Functioning (ODF) scores differ between individuals diagnosed with Depressive Disorder and nonclinical control groups (i.e., without a psychiatric diagnosis)? (b) Do mature defenses, non-mature defenses, ODF scores change during a psychological intervention in patients diagnosed with Depressive Disorder? MATERIALI E METODI: The present protocol has been registered on PROSPERO (CRD42023442620). Primary research articles and grey literature exploring defensive functioning in Depressive Disorders with reliable measures will be searched on EBSCO/PsycINFO, Web of Science, and PubMed. Two independent reviewers will conduct the search and assess the quality of the included studies with the Newcastle-Ottawa Scale. Meta-analytic synthesis (SMD) and sensitivity analysis will be performed. The study will be conducted according with \nthe PRISMA 2020 guidelines. RISULTATI: We expect: (a) Lower mature defenses, higher non-mature defenses, and lower ODF scores in individuals with Depressive Disorders in comparison to nonclinical control groups; (b) Higher mature defenses, lower non-mature defenses, and higher ODF scores \nover the course of a psychological intervention in patients with Depressive Disorders. CONCLUSIONI: Improving clinical understanding of defense mechanisms may foster therapeutic process with depressed patients (De Roten et al., 2021). Potential biases and gaps in the available literature will be addressed leading to research recommendations.
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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 | 0.080 | 0.090 |
| Meta-epidemiology (narrow) | 0.006 | 0.005 |
| Meta-epidemiology (broad) | 0.019 | 0.027 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.050 | 0.006 |
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".