Bibliographic record
Abstract
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 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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".