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Record W7097673989

In Review Personality and Depression

2015· article· en· W7097673989 on OpenAlexaboutno aff

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldPsychology
TopicPersonality Disorders and Psychopathology
Canadian institutionsnot available
Fundersnot available
KeywordsPersonalityPersonality pathologyPersonality disordersDepression (economics)Personality Assessment InventoryBig Five personality traitsAssociation (psychology)
DOInot available

Abstract

fetched live from OpenAlex

linicians and researchers alike have long noted the preva-lence of personality pathology in individuals with MDD. Indeed, the clarification of the personality features associated with major depression, and the implications of these associa-tions for the understanding and care of major depression, have been the focus of much empirical work. Which personality features are common to individuals with MDD? What is the causal significance of this cooccurrence? Can clinicians make use of personality information during the assessment or treat-ment of depressed individuals? This review aims to shed some light upon these questions, through a review of personality models and their associations with major depression, the etio-logical connections that may underlie these associations, and the implications that personality may have for the diagnosis La Revue canadienne de psychiatrie, vol 53, no 1, janvier 200814 Objective: To examine the implications of the association between personality and depression for the understanding, assessment, and treatment of major depression. Method: A broad range of peer-reviewed manuscripts relevant to personality and depression was reviewed. Particular emphasis was placed on etiology, stability, diagnosis, and treatment implications. Results: Personality features in depressed samples reliably differ from those of healthy samples. The associations between personality and depression are consistent with a variety of causal models; these models can best be compared through longitudinal research. Research demonstrates that attention to personality features can be useful in diagnosis and treatment. Indeed, personality information has been on the forefront of recent efforts to advance the current diagnostic classification system. Moreover, personality dimensions have shown recent promise in the prediction of differential treatment outcome. For example, neuroticism is associated with preferential response to pharmacotherapy rather than psychotherapy. Conclusions: Consideration of personality features is crucial to the understanding and management 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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.020
Threshold uncertainty score0.066

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0030.004
Science and technology studies0.0010.001
Scholarly communication0.0030.002
Open science0.0020.001
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0200.008

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 head0.075
GPT teacher head0.394
Teacher spread0.319 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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

Citations0
Published2015
Admission routes1
Has abstractyes

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