Revisiting presenteeism to broaden its conceptualization: A qualitative study
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.023 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.011 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".