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Record W2023629506 · doi:10.1097/nmd.0b013e3182043b4e

Defensive Flexibility and Its Relation to Symptom Severity, Depression, and Anxiety

2011· article· en· W2023629506 on OpenAlexaff
Martin Drapeau, Yves de Roten, Emily Blake, Véronique Beretta, Micha Strack, Annett Körner, Jean‐Nicolas Despland

Bibliographic record

VenueThe Journal of Nervous and Mental Disease · 2011
Typearticle
Languageen
FieldPsychology
TopicPersonality Disorders and Psychopathology
Canadian institutionsMcGill University
Fundersnot available
KeywordsPsychologyClinical psychologyPsychopathologyAnxietyFlexibility (engineering)ChecklistDepression (economics)HamdMental healthRating scalePsychiatryDevelopmental psychologyCognitive psychology

Abstract

fetched live from OpenAlex

Numerous studies have examined which individual defense mechanisms are related with mental health, and which are linked with psychopathology. However, the idea that a flexible use of defensive mechanisms is related to psychological wellbeing remained a clinical assumption, which this study sought to test empirically. A total of 62 (N = 62) outpatients participated in the study and were assessed with the Symptom Checklist-90R and the Social Adjustment Self-rated Scale. A subsample of 40 participants was further assessed using the Hamilton Depression (HAMD-21) and Anxiety scales (HAMA-21). The first therapy session of all participants was transcribed and rated using the Defense Mechanisms Ratings Scales (), and the Overall Defensive Functioning (ODF) score, which indicates the maturity of one's defensive functioning, was computed. An indicator of flexible use of defenses was also calculated based on the Gini Concentration C measure. Results showed that defensive flexibility, but not ODF, could predict anxiety scores. Symptom severity was predicted by both ODF and defensive flexibility, although in directions opposite to our predictions. Results suggest that defensive flexibility captures another aspect of an individual's functioning not assessed by the ODF, and that it is a promising new way of documenting defensive functioning.

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 score0.117
Threshold uncertainty score0.261

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 head0.032
GPT teacher head0.305
Teacher spread0.273 · 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

Citations12
Published2011
Admission routes1
Has abstractyes

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