The health-performance framework of presenteeism: Towards understanding an adaptive behaviour
Bibliographic record
Abstract
The substantial health and financial costs of presenteeism are well-documented. Paradoxically, presenteeism also has a positive side, which has been largely overlooked. Emerging evidence shows that presenteeism can be a choice that offers a range of positive benefits to the ‘presentee’ (an employee who works through illness). In this conceptual article, we view presenteeism as purposeful and adaptive behaviour: a dynamic process that serves the purpose of balancing health constraints and performance demands in tandem. We propose a 2×2 framework of presenteeism (therapeutic, functional, overachieving, and dysfunctional) and suggest that the success of the presenteeism adaptation process depends on the availability of internal capacities and flexible work resources. When the workplace is supportive and provides adequate resources to aid adaptation, presenteeism can be a sustainable choice for maintaining performance under impaired health. We examine the role of resources for functional presenteeism by drawing on conservation of resources theory and self-determination theory. This framework can contribute to a better understanding of presenteeism by viewing it as an adaptive process, considering presentees as heterogeneous groups, and exploring the importance of internal and work resources for balancing health and performance demands. It sketches new avenues for research and practice and the effective management of presenteeism, health, and performance.
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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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.033 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".