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Record W1994882433 · doi:10.1521/pedi.18.5.448.51328

A Comparison of Personality Function Among Patients with Seasonal Depression, Nonseasonal Depression, and Nonclinical Participants

2004· article· en· W1994882433 on OpenAlexaff
Erin E. Michalak, Kerry L. Jang, Edwin M. Tam, Lakshmi N. Yatham, Raymond W. Lam, W. John Livesley

Bibliographic record

VenueJournal of Personality Disorders · 2004
Typearticle
Languageen
FieldPsychology
TopicMental Health Research Topics
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsPsychologyDepression (economics)PersonalityPsychopathologyClinical psychologyPersonality disordersPersonality pathologyPsychiatryPsychoanalysis

Abstract

fetched live from OpenAlex

Although a large body of research has accumulated concerning the relationship between nonseasonal depression and personality, comparatively few studies have examined the relationship between seasonal affective disorder (SAD) and personality. This study compared dimensional aspects of personality in patients diagnosed with SAD (N = 60), nonseasonal depression (N = 273), and nonclinical controls (N = 297) using the Dimensional Assessment of Personality Pathology (DAPP-BQ; Livesley & Jackson, in press). Analysis by ANCOVA indicated that significant between-group differences occurred in several of the 18 DAPP-BQ dimensions, with patients with SAD exhibiting personality psychopathology that was intermediate between the nonclinical sample and patients with nonseasonal depression. The results demonstrated that the traits associated with seasonal and nonseasonal depression differ in degree, not kind.

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)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.075
GPT teacher head0.436
Teacher spread0.362 · 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 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

Citations3
Published2004
Admission routes1
Has abstractyes

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