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Record W4403067222 · doi:10.1002/jclp.23744

Depressive symptoms and goal pursuit: Between‐person and reciprocal within‐person effects in a multi‐wave longitudinal study

2024· article· en· W4403067222 on OpenAlexafffund
Isabelle Leduc‐Cummings, Marina Milyavskaya, Martin Drapeau

Bibliographic record

VenueJournal of Clinical Psychology · 2024
Typearticle
Languageen
FieldPsychology
TopicPersonality Traits and Psychology
Canadian institutionsCarleton UniversityMcGill University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsPsychologyGoal pursuitReciprocalInterpersonal communicationGoal orientationPerceptionLongitudinal studyDepressive symptomsInterpersonal relationshipDevelopmental psychologySocial psychologyClinical psychologyCognitionPsychiatryStatistics

Abstract

fetched live from OpenAlex

INTRODUCTION: Depressive symptoms, goal progress, and goal characteristics are interrelated, but the directionality of these relationships is unclear. METHODS: In a 6-wave longitudinal study (N = 431; 2002 total surveys), we examine the bidirectionality of the relationships between depressive symptoms, goal characteristics (commitment, self-efficacy, and perception of other's support), and goal progress for academic and interpersonal goals at 2-week intervals. Separate random-intercept cross-lagged panel models were tested for each goal characteristic across both goals. RESULTS: At the within-person level, goal progress significantly positively predicted commitment, self-efficacy, and perception of others' support for the goal. Most of the other hypothesized paths were nonsignificant, including paths between depressive symptoms and progress. At the between-person level, all variables were significantly correlated, with some effects significantly larger for the interpersonal than the academic goal. DISCUSSION: The results suggest that when it comes to depressive symptoms and goal pursuit, general tendencies may be more important than variations over 2-week intervals.

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)0.005
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.120
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.003
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 head0.313
GPT teacher head0.533
Teacher spread0.220 · 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.

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

Citations4
Published2024
Admission routes2
Has abstractyes

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