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Record W2948277477

Implicit theories of depression and depression severity influence endorsement of lifestyle changes and therapies for depression

2017· article· en· W2948277477 on OpenAlexaff
Karlen Hutlet, Adelle Andrews

Bibliographic record

VenueStudent Research Proceedings · 2017
Typearticle
Languageen
FieldPsychology
TopicBehavioral Health and Interventions
Canadian institutionsMacEwan University
Fundersnot available
KeywordsMindsetDepression (economics)PsychologyClinical psychologyPsychotherapistPsychiatry
DOInot available

Abstract

fetched live from OpenAlex

Implicit theories refer to the extent to which an attribute of self is viewed as changeable (Dweck, 1999). Individuals who have an entity theory, or a fixed mindset, of depression believe that depression cannot change. Conversely, those who have an incremental theory, or a growth mindset, of depression believe that depression can change. The current study (N = 235) investigated whether implicit theories of depression are predictive of depression severity in undergraduate students. Results showed that a growth mindset of depression, being male, and endorsing lifestyle changes for depression were predictive of lower depression severity. Understanding implicit theories of depression may improve therapeutic outcomes. If individuals are led to view depression as changeable prior to therapy, they may be more likely to believe that therapy can improve depression. Therefore, such a treatment component may be associated with increased therapeutic engagement and enhanced therapeutic outcome. Discipline: Statistics Faculty Mentor: Dr. Karen Buro

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)0.000
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 score0.065
Threshold uncertainty score0.559

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.001
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 head0.121
GPT teacher head0.517
Teacher spread0.395 · 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

Citations0
Published2017
Admission routes1
Has abstractyes

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