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Record W2947194278 · doi:10.1177/1073191119851574

Positive and Negative Activation in the Mood Disorder Questionnaire: Associations With Psychopathology and Emotion Dysregulation in a Clinical Sample

2019· article· en· W2947194278 on OpenAlexaff
Ryan W. Carpenter, Kasey Stanton, Noah N. Emery, Mark Zimmerman

Bibliographic record

VenueAssessment · 2019
Typearticle
Languageen
FieldMedicine
TopicBipolar Disorder and Treatment
Canadian institutionsWestern University
FundersNational Institute on Drug AbuseNational Institute on Alcohol Abuse and Alcoholism
KeywordsPsychologyPsychopathologyClinical psychologyMoodBehavioral activationBipolar disorderEmotional dysregulationMood disordersConfirmatory factor analysisPsychiatryStructural equation modelingAnxietyCognition

Abstract

fetched live from OpenAlex

The Mood Disorder Questionnaire is a screening measure for bipolar disorder, previously found to comprise separate Positive and Negative Activation subscales. We sought to replicate these factors and examine their associations with a range of psychopathology. To further explicate the nature of Negative Activation, we examined associations with the Difficulties in Emotion Regulation Scale, a measure of emotion dysregulation. The sample consisted of 1,787 participants from an outpatient treatment facility. Confirmatory factor analysis replicated the existence of Positive and Negative Activation subscales. Logistic regressions, as hypothesized, found that Positive Activation was positively associated only with bipolar disorder, while Negative Activation was associated with almost all disorders. The Impulse and Goals subscales of the Difficulties in Emotion Regulation Scale were uniquely associated with Negative Activation, suggesting it may specifically assess impulsive behavior in emotional situations. The findings suggest that it may be important to attend to both Mood Disorder Questionnaire subscales.

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 score0.016
Threshold uncertainty score0.208

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 head0.013
GPT teacher head0.349
Teacher spread0.336 · 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
Published2019
Admission routes1
Has abstractyes

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