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Record W4310149612 · doi:10.1037/apl0001066

Quantifying the evidence for the absence of the job demands and job control interaction on workers’ well-being: A Bayesian meta-analysis.

2022· review· en· W4310149612 on OpenAlexaff
Karoline Huth, Greg A. Chung‐Yan

Bibliographic record

VenueJournal of Applied Psychology · 2022
Typereview
Languageen
FieldHealth Professions
TopicWorkplace Health and Well-being
Canadian institutionsUniversity of Windsor
Fundersnot available
KeywordsPsycINFOPsychologyMeta-analysisJob attitudeJob performanceJob controlOccupational stressJob analysisJob designWell-beingJob satisfactionControl (management)Applied psychologyJob characteristic theoryRaw dataSocial psychologyMEDLINEComputer scienceMedicine

Abstract

fetched live from OpenAlex

Central to many influential theories in the occupational health and stress literature is that job resources reduce the negative effects of job demands on workers' well-being. However, empirical investigations testing this supposition have produced inconsistent findings. This study evaluates the interaction between job demands and job control on workers' well-being through a systematic literature search and using a Bayesian meta-analytic approach. Both aggregated study findings and raw participant-level data were included in the study, resulting in 104 effect sizes of aggregate-level data and 14 participant-level data sets. Overall, the data provided strong evidence for the absence of an interaction between job demands and job control. Longitudinal and nonlinear research designs were also examined but did not alter this overall conclusion. Contrary to the postulations of widespread theories, job control does not reduce the negative impact of job demands on workers' well-being. Alternative theoretical approaches and the need for more consistent and rigorous research standards, like open science practices, are discussed. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

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)0.010
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.826
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0100.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0030.002
Bibliometrics0.0000.001
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.004
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 head0.294
GPT teacher head0.526
Teacher spread0.233 · 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.

Study designNot applicable
Domainnot available
GenreReview

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

Citations28
Published2022
Admission routes1
Has abstractyes

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