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Record W2952165753 · doi:10.1177/0894845319851875

Does Career Resilience Promote Subjective Well-Being? Mediating Effects of Career Success and Work Stress

2019· article· en· W2952165753 on OpenAlexaff
Yu Han, Tasnuva Chaudhury, Greg J. Sears

Bibliographic record

VenueJournal of Career Development · 2019
Typearticle
Languageen
FieldPsychology
TopicPsychological Well-being and Life Satisfaction
Canadian institutionsCarleton University
Fundersnot available
KeywordsSalaryPsychological resiliencePsychologyJob satisfactionCareer developmentLife satisfactionWell-beingWork (physics)Resilience (materials science)Applied psychologySocial psychologyPolitical science

Abstract

fetched live from OpenAlex

Drawing on the “top-down” view of life satisfaction, this study investigates the influence of employee career resilience on life satisfaction and examines mediating effects of indicators of career success (i.e., salary, job level, job satisfaction) and work-related well-being (i.e., work stress) on this relationship. Data were collected from a sample of 527 working professionals from various organizations across the central United States. Results revealed that career resilience was positively associated with life satisfaction. Two indicators of career success (job satisfaction and salary) and work stress were found to mediate this relationship. Taken together, these findings signal that career resilience contributes to employee subjective well-being and that both career success and work stress are instrumental in explaining this relationship.

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 score0.003
Threshold uncertainty score0.011

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 studies0.0010.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.009
GPT teacher head0.260
Teacher spread0.251 · 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

Citations46
Published2019
Admission routes1
Has abstractyes

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