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Record W3000544284 · doi:10.1097/nmd.0000000000001112

Change in Defense Mechanisms and Depression in a Pilot Study of Antidepressive Medications Plus 20 Sessions of Psychotherapy for Recurrent Major Depression

2020· article· en· W3000544284 on OpenAlexaff
J. Christopher Perry, Elisabeth Banon, Michael Bond

Bibliographic record

VenueThe Journal of Nervous and Mental Disease · 2020
Typearticle
Languageen
FieldMedicine
TopicTreatment of Major Depression
Canadian institutionsJewish General Hospital
Fundersnot available
KeywordsDepression (economics)Rating scalePsychologyBeck Depression InventoryClinical psychologyMajor depressive disorderPsychiatryDepressive symptomsCognitionDevelopmental psychologyAnxiety

Abstract

fetched live from OpenAlex

Treatment studies of major depression commonly focus on symptoms, leaving aside change in putative psychological risk factors. This pilot study examines the relationship between changes in eight depressive defenses and depressive symptoms. Twelve adults with acute recurrent major depression were given antidepressive medications and randomized to 20 sessions of either cognitive behavioral therapy or dynamic psychotherapy and followed for 1 year. Defenses were assessed using the Defense Mechanism Rating Scales (DMRS) and Defense Style Questionnaire (DSQ) at intake, termination, and 1-year follow-up. Depression improved highly significantly on both the Hamilton Rating Scale for Depression and Beck Depression Inventory, respectively, eight (67%) and nine (75%) patients attained recovery by 1 year. Depressive defenses improved significantly by termination (mean ES = 0.97; 95% confidence interval, 0.30-2.16), but retrogressed somewhat by 1 year. A mean of 12.17% (SD = 10.60) depressive defenses remained; only five subjects (50%) attained normative levels. Although causal relationships were not established, depressive defenses are promising candidates for mediating treatment effects on outcome of major depression.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.362
Threshold uncertainty score0.372

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.051
GPT teacher head0.338
Teacher spread0.287 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations30
Published2020
Admission routes1
Has abstractyes

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