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Record W2022413946 · doi:10.1037/abn0000031

A two-factor model of relapse/recurrence vulnerability in unipolar depression.

2015· article· en· W2022413946 on OpenAlexaff
Norman A. S. Farb, Julie Irving, Adam K. Anderson, Zindel V. Segal

Bibliographic record

VenueJournal of Abnormal Psychology · 2015
Typearticle
Languageen
FieldPsychology
TopicMental Health Research Topics
Canadian institutionsThe Scarborough HospitalCentre for Addiction and Mental HealthUniversity of Toronto
FundersNational Institute of Mental Health
KeywordsPsychologyRuminationClinical psychologyVulnerability (computing)Dysfunctional familyDepression (economics)Major depressive disorderPsychopathologyPsychiatryDevelopmental psychologyCognitionMood

Abstract

fetched live from OpenAlex

The substantial health burden associated with major depressive disorder (MDD) is a product of both its high prevalence and the significant risk of relapse, recurrence, and chronicity. Establishing recurrence vulnerability factors (VFs) could improve the long-term management of MDD by identifying the need for further intervention in seemingly recovered patients. We present a model of sensitization in depression vulnerability, with an emphasis on the integration of behavioral and neural systems accounts. Evidence suggests that VFs fall into 2 categories: dysphoric attention and dysphoric elaboration. Dysphoric attention is driven by fixation on negative life events, and is characterized behaviorally by reduced executive control, and neurally by elevated activity in the brain's salience network. Dysphoric elaboration is driven by rumination that promotes overgeneral self- and contextual appraisals, and is characterized behaviorally by dysfunctional attitudes, and neurally by elevated connectivity within normally distinct prefrontal brain networks. Although few prospective VF studies exist from which to catalogue a definitive neurobehavioral account, extant data support the value of the proposed 2-factor model. Measuring the continued presence of these 2 VFs during recovery may more accurately identify remitted patients who would benefit from targeted prophylactic intervention.

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)0.003
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 score0.089
Threshold uncertainty score0.593

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.200
GPT teacher head0.490
Teacher spread0.290 · 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

Citations75
Published2015
Admission routes1
Has abstractyes

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