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Why Do Employees Work When Ill? A Study of the Antecedents and Outcomes of Presenteeism

2012· article· en· W2901944072 on OpenAlexaff
Stephen Deery, Roderick D. Iverson, Janet Walsh

Bibliographic record

VenueAcademy of Management Proceedings · 2012
Typearticle
Languageen
FieldHealth Professions
TopicWorkplace Health and Well-being
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsPresenteeismAbsenteeismPsychologyAttendanceAutonomySocial psychologyNursingMedicinePolitical science

Abstract

fetched live from OpenAlex

Presenteeism or attending work while ill is said to be a growing organizational problem. This study examined the antecedents of presenteeism as well as its effects on subsequent absenteeism. Based on a sample of emergency service contact employees (N=241) the research found that in addition to perceptions of overall health, job demands, attendance enforcement policies and autonomy predicted presenteeism. In turn presenteeism was associated with higher emotional exhaustion and higher absenteeism. The findings suggested that contextual rather than personal factors influenced employees’ decisions to attend work when ill and that presenteeism had negative consequences for employees in terms of emotional strain and subsequent absenteeism.

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)0.001
metaresearch head score (Gemma)0.000
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 score0.012
Threshold uncertainty score0.518

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 science0.0000.001
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 head0.041
GPT teacher head0.373
Teacher spread0.332 · 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

Citations1
Published2012
Admission routes1
Has abstractyes

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