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Record W2896307113 · doi:10.1108/joepp-02-2018-0007

To be or not to be…at work while ill

2018· article· en· W2896307113 on OpenAlexaff
James MacGregor, John Cunningham

Bibliographic record

VenueJournal of Organizational Effectiveness People and Performance · 2018
Typearticle
Languageen
FieldHealth Professions
TopicWorkplace Health and Well-being
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsPresenteeismAbsenteeismCLARITYOperationalizationProductivityOriginalityPsychologyTest (biology)Public healthPublic relationsValue (mathematics)Social psychologyPublic sectorWork (physics)Sample (material)NursingMedicinePolitical scienceEconomicsEconomic growthEngineering

Abstract

fetched live from OpenAlex

Purpose The purpose of this paper is to analyze the results from two public sector organizations to test a model of the organizational antecedents and health consequences of sickness presenteeism (SP) in the workplace. Design/methodology/approach The study reports on two surveys of public employees, one including 237 respondents and another of 391 employees. The combined sample allowed for the testing of a model of organizational antecedents and the health consequences of SP. Findings The results supported the model, indicating that increased leader support and goal clarity decrease SP indirectly through increased trust. Decreasing presenteeism is associated with decreased sickness absence and better health. Practical implications The key practical application is in encouraging managers and scholars to recognize that the costs of presenteeism are as higher or higher than the costs of absenteeism. Social implications The social implications are clear in helping us recognize that when people come to work sick, they are not productive and are endangering the productivity of others. Originality/value This is the first time that research had defined and operationalized a causal model linking antecedents such as leader-member relations, goal clarity and trust with SP and 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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.023
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.027
GPT teacher head0.350
Teacher spread0.323 · 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 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

Citations7
Published2018
Admission routes1
Has abstractyes

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