Examining the antecedents and conceptualisations of presenteeism and absenteeism in the workplace and its impact on teleworkers: a scoping review protocol
Bibliographic record
Abstract
Presenteeism and absenteeism impact an individual’s ability to work efficiently. The onset of the COVID-19 pandemic has exacerbated their effect on teleworkers. The current literature regarding presenteeism and absenteeism is broad, with various definitions of both phenomena making it difficult for researchers to measure accurately. This scoping review protocol aims to examine existing definitions of presenteeism and absenteeism in the workplace and focus on the antecedents for why they occur among teleworkers. The scoping review protocol has been pre-registered on Open Science Framework (osf.io/ur5a6). Online databases MEDLINE, CINAHL, PsycINFO, ABI Inform Global, SCOPUS, Web of Science and Business Source Premier will be searched to identify studies investigating presenteeism and absenteeism. Inclusion criteria will include individuals 18 or older who are part of the working population and currently employed in a teleworking environment for at least 50% of the working hours. The findings of this review will be of interest to companies and health professionals who seek to develop more practical policies and guidelines to assist those who engage in presenteeism and absenteeism.
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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 | 0.095 | 0.119 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.011 | 0.012 |
| Bibliometrics | 0.021 | 0.017 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.007 | 0.005 |
| Insufficient payload (model declined to judge) | 0.045 | 0.006 |
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".