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Record W4321479984 · doi:10.1017/9781009268332

Organizational Stress and Well-Being

2023· book· en· W4321479984 on OpenAlexaff
Laurent Lapierre, Paul E. Spector, Peter Y. Chen, Johannés Siegrist, Dorian Hartlaub, Amanda J. Hancock, Wendy J. Casper, Zara Whysall, Wilmar B. Schaufeli, Marisa Salanova, Hope Dodd, Kevin Daniels, Leslie B. Hammer, Kimberly E. O’Brien, Monideepa Tarafdar

Bibliographic record

VenueCambridge University Press eBooks · 2023
Typebook
Languageen
FieldEconomics, Econometrics and Finance
TopicCOVID-19 Pandemic Impacts
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsPsychologyWell-beingPsychological interventionOccupational stressApplied psychologyStress managementPublic relationsHuman resource managementKnowledge managementSocial psychologyPolitical scienceComputer scienceClinical psychology

Abstract

fetched live from OpenAlex

In this Cambridge Companion, global thought leaders in the fields of workplace stress and well-being highlight how theory and research can improve employee health and well-being. The volume explains how and why the topics of workplace stress and well-being have evolved and continue to be highly relevant, and why line managers have great influence over employees' quality of working life. It includes the latest research findings on stress and well-being and their impact on organizations, as well as up-to-date findings on the effectiveness of workplace interventions focused on these issues. It also explores important and emerging issues relating to organizational stress and well-being, including the ongoing effects of the global coronavirus pandemic. This is an ideal reference for students and researchers in the areas of human resources management, occupational health psychology and organisational behavior.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.920
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.024
GPT teacher head0.195
Teacher spread0.171 · 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
GenreOther

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

Citations13
Published2023
Admission routes1
Has abstractyes

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