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Record W4417277247 · doi:10.1080/10400419.2025.2594357

Creativity Under Stress at Work: A Person-Centered Approach

2025· article· en· W4417277247 on OpenAlexfundno aff
Shengjie Lin, Zorana Ivčević, Marc A. Brackett

Bibliographic record

VenueCreativity Research Journal · 2025
Typearticle
Languageen
FieldPsychology
TopicCreativity in Education and Neuroscience
Canadian institutionsnot available
FundersOntario Hospital Association
KeywordsCreativityStress (linguistics)Creativity techniqueCoping (psychology)

Abstract

fetched live from OpenAlex

Work exposes people to different kinds of stressors and provides different resources. Building on the job demands-resources theory (JD-R), the current study examined intrapersonal patterns of job stressors (challenge and hindrance demands) and personal and job resources (i.e. passion and voice) and their relationship with creativity outcomes (creative self-efficacy and creative behavior) in a sample of hospital workers (N = 5,066). Latent profile analyses identified five groups with distinct patterns of job demands and resources, in which stress-related demands were paired with either high or low resources. The creativity-related outcomes were similarly high in Low Stressor Demands-Moderate Resources and High Stressor Demands-Moderate Resources groups, suggesting that resources play an important role in creativity when stress-related demands are high. This research highlights the benefits of using a person-centered approach for developing a better understanding of how stress-related demands interact with resources within individuals.

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)0.004
metaresearch head score (Gemma)0.003
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 score0.005
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0020.004
Scholarly communication0.0050.002
Open science0.0010.003
Research integrity0.0010.002
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 head0.330
GPT teacher head0.492
Teacher spread0.162 · 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

Citations1
Published2025
Admission routes1
Has abstractyes

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