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Record W3097958108 · doi:10.1002/jclp.23079

Perfectionism, efficacy, and daily coping and affect in depression over 6 months

2020· article· en· W3097958108 on OpenAlexafffund
Alexandra Richard, David M. Dunkley, David C. Zuroff, Molly Moroz, Joan E. Foley, Maxim Lewkowski, Gail Myhr, Ruta Westreich

Bibliographic record

VenueJournal of Clinical Psychology · 2020
Typearticle
Languageen
FieldPsychology
TopicPerfectionism, Procrastination, Anxiety Studies
Canadian institutionsMcGill University Health CentreMcGill UniversityJewish General Hospital
FundersFonds de Recherche du Québec - SantéFonds de Recherche du Québec-Société et CultureJewish General HospitalSocial Science Research Council
KeywordsPsychologyAffect (linguistics)Coping (psychology)Clinical psychologyPerfectionism (psychology)Depression (economics)Psychotherapist

Abstract

fetched live from OpenAlex

OBJECTIVE: This study examined how perfectionism and efficacy impacted the maintenance of daily coping and affect in depression over six months. METHOD: Forty-six depressed patients (69.6% female, mean age = 41.11 years) completed measures of perfectionism dimensions (self-critical, personal standards), efficacy, and depressive severity at Time 1. Participants then completed daily diaries of stress appraisals, coping, and affect for 7 consecutive days at Time 1 and Time 2, 6 months later. RESULTS: Perfectionism dimensions and efficacy were differentially correlated with appraisals, coping, and affect across Times 1 and 2. Behavioral disengagement tendencies mediated the relation between self-critical perfectionism and daily negative affect over 6 months, controlling for depressive severity. Efficacy was related to daily positive affect over 6 months through problem-focused coping tendencies. CONCLUSIONS: Results highlight the importance of addressing perfectionism, efficacy, and daily coping tendencies to more effectively reduce distress and bolster resilience in people with 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 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)0.001
metaresearch head score (Gemma)0.001
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 score0.046
Threshold uncertainty score0.589

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 science0.0000.000
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 head0.130
GPT teacher head0.488
Teacher spread0.358 · 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

Citations6
Published2020
Admission routes2
Has abstractyes

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