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Record W2120446121

Relationships between job stress and worker perceived responsibilities and job characteristics.

2011· article· en· W2120446121 on OpenAlexaffabout
Carolyn S. Dewa, Angus H. Thompson, Philip Jacobs

Bibliographic record

VenuePubMed · 2011
Typearticle
Languageen
FieldHealth Professions
TopicWorkplace Health and Well-being
Canadian institutionsCentre for Addiction and Mental Health
Fundersnot available
KeywordsJob stressPsychologyAffect (linguistics)Job attitudeLogistic regressionWork (physics)Psychological interventionJob satisfactionJob performanceJob designDemographic economicsSocial psychologyApplied psychologyMedicineEngineeringEconomics
DOInot available

Abstract

fetched live from OpenAlex

BACKGROUND: Few studies have examined the relationship between perceived responsibilities by workers and job characteristics and experiences of stress. OBJECTIVE: To examine the relationship between job stress and work responsibilities and job characteristics. METHODS: We analyzed data from 2737 adults who were labor force participants in the province of Alberta, Canada. A logistic regression model was employed to examine factors associated with high job stress. RESULTS: About 18% of the studied workers considered their job as being "highly stressful." Workers who were male, did not consider their job a career or who were highly satisfied with their jobs were significantly less likely to identify their jobs as "highly stressful." The probability of describing a job as "highly stressful" significantly increased as workers perceived their actions have an affect on those around them or when their jobs required additional or variable hours. CONCLUSION: A number of factors are associated with experiencing high work stress including being more engaged with work. This is an important finding for employers, offering insight into where interventions may be targeted.

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)0.002
metaresearch head score (Gemma)0.001
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 score0.017
Threshold uncertainty score0.865

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.110
GPT teacher head0.327
Teacher spread0.218 · 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

Citations26
Published2011
Admission routes2
Has abstractyes

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