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Record W2404732890 · doi:10.1037/apl0000044

Are anxious workers less productive workers? It depends on the quality of social exchange.

2015· article· en· W2404732890 on OpenAlexafffund
Julie M. McCarthy, John P. Trougakos, Bonnie Hayden Cheng

Bibliographic record

VenueJournal of Applied Psychology · 2015
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicJob Satisfaction and Organizational Behavior
Canadian institutionsThe Scarborough Hospital
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsPsychologyEmotional exhaustionAnxietySocial psychologyConservation of resources theorySocial exchange theoryJob performanceJob satisfactionClinical psychologyBurnout

Abstract

fetched live from OpenAlex

In this article, we draw from Conservation of Resources Theory to advance and test a framework which predicts that emotional exhaustion plays an explanatory role underlying the relation between workplace anxiety and job performance. Further, we draw from social exchange theories to predict that leader-member exchange and coworker exchange will mitigate the harmful effects of anxiety on job performance. Findings across a 3-wave study of police officers supported our model. Emotional exhaustion mediated the link between workplace anxiety and job performance, over and above the effect of cognitive interference. Further, coworker exchange mitigated the positive relation between anxiety and emotional exhaustion, while leader-member exchange mitigated the negative relation between emotional exhaustion and job performance. This study elucidates the effects of workplace anxiety on resource depletion via emotional exhaustion and highlights the value of drawing on social resources to offset the potentially harmful effects of workplace anxiety on job performance.

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)0.002
metaresearch head score (Gemma)0.008
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 score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.002
Scholarly communication0.0020.001
Open science0.0000.002
Research integrity0.0010.001
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 head0.102
GPT teacher head0.352
Teacher spread0.250 · 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

Citations307
Published2015
Admission routes2
Has abstractyes

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