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Record W3207429440 · doi:10.3233/wor-213591

Revisiting presenteeism to broaden its conceptualization: A qualitative study

2021· article· en· W3207429440 on OpenAlexaff
Mouna Knani, Pierre‐Sébastien Fournier, Caroline Biron

Bibliographic record

VenueWork · 2021
Typearticle
Languageen
FieldHealth Professions
TopicWorkplace Health and Well-being
Canadian institutionsUniversité LavalHEC Montréal
Fundersnot available
KeywordsPresenteeismConceptualizationQualitative researchPsychologyContext (archaeology)Thematic analysisSocial psychologyApplied psychologySociologyComputer scienceAbsenteeism

Abstract

fetched live from OpenAlex

BACKGROUND: Presenteeism is generally viewed as a symptom of organizational or individual dysfunction and is rarely considered as a behavioral response to positive triggering factors. Our study examines this issue in small enterprises (SEs), which are an unexplored environment in terms of presenteeism. OBJECTIVE: Through in-depth analysis, this study aims to understand the positive and negative factors that impact presenteeism in the context of small and medium enterprises (SMEs), with a particular focus on SEs. METHODS: We adopt a qualitative methodological approach in which we conducted 17 semi-structured interviews with employees and owner-managers of SEs with between 20 and 49 employees. RESULTS: Our thematic analysis shows that presenteeism can be explained by factors related to pressure to attend work, by individuals' constraints and commitment, by organizational and individual characteristics and by a congenial work environment. Presenteeism can also be a type of "therapy" which helps individuals to avoid focusing on being sick and enables them to stay active and avoid social isolation. CONCLUSIONS: Our study differs from earlier research by providing a more in-depth analysis of the positive and negative factors that trigger presenteeism. This article will help to expand the current theoretical knowledge about presenteeism and encourage a more holistic interpretation of the phenomenon.

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)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.134
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
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 integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.002

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 head0.086
GPT teacher head0.489
Teacher spread0.403 · 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; both teacher heads agree on what is shown here.

Study designQualitative
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
Published2021
Admission routes1
Has abstractyes

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