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Record W4229040532 · doi:10.5539/gjhs.v14n5p10

Creative Self-Efficacy as a Predictor of the Use of Creative Cognition

2022· article· en· W4229040532 on OpenAlexvenueno aff
Wu‐jing He

Bibliographic record

VenueGlobal Journal of Health Science · 2022
Typearticle
Languageen
FieldPsychology
TopicCreativity in Education and Neuroscience
Canadian institutionsnot available
FundersEducation University of Hong Kong
KeywordsPsychologyCognitionSelf-efficacySocial cognitive theoryPromotion (chess)Perspective (graphical)Scale (ratio)Social cognitionCreative thinkingEmpirical researchVariance (accounting)Social psychologyCreativityComputer science

Abstract

fetched live from OpenAlex

The present study examined the hypothesized predictive role of creative self-efficacy in the use of creative cognition by taking a perspective rooted in social cognitive theory. A sample of 614 undergraduate students (51.6% female) in Hong Kong was surveyed using the Creative Self-efficacy Scale and the Use of Creative Cognition Scale. The results of multiple regression analyses indicated that creative self-efficacy significantly accounted for 11% of the variance in the tendency to deploy creative cognition. The results of Pearson correlation analysis suggested that the strength of the association between creative self-efficacy and the tendency to deploy creative cognition was of medium size (r = .45). These findings lend empirical support to social cognitive theory and the creative behavior as agentic action (CBAA) model. The findings also suggest the practical implication that creative intention can be facilitated through the promotion of a stronger sense of creative self-belief.

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)0.001
metaresearch head score (Gemma)0.005
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 score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.085
GPT teacher head0.421
Teacher spread0.335 · 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

Citations4
Published2022
Admission routes1
Has abstractyes

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