Going to work ill: A meta-analysis of the correlates of presenteeism and a dual-path model.
Bibliographic record
Abstract
Interest in presenteeism, attending work while ill, has flourished in light of its consequences for individual well-being and organizational productivity. Our goal was to identify its most significant causes and correlates by quantitatively summarizing the extant research. Additionally, we built an empirical model of some key correlates and compared the etiology of presenteeism versus absenteeism. We used meta-analysis (in total, K = 109 samples, N = 175,965) to investigate the correlates of presenteeism and meta-analytic structural equation modeling to test the empirical model. Salient correlates of working while ill included general ill health, constraints on absenteeism (e.g., strict absence policies, job insecurity), elevated job demands and felt stress, lack of job and personal resources (e.g., low support and low optimism), negative relational experiences (e.g., perceived discrimination), and positive attitudes (satisfaction, engagement, and commitment). Moreover, our dual process model clarified how job demands and job and personal resources elicit presenteeism via both health impairment and motivational paths, and they explained more variation in presenteeism than absenteeism. The study sheds light on the controversial act of presenteeism, uncovering both positive and negative underlying mechanisms. The greater variance explained in presenteeism as opposed to absenteeism underlines the opportunities for researchers to meaningfully investigate the behavior and for organizations to manage it. (PsycINFO Database Record
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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 | 0.025 | 0.045 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.036 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".