Investigating Associations Between Physical Activity and Presenteeism – A Scoping Review
Bibliographic record
Abstract
OBJECTIVE: The aim of this study is to scope the literature on what is currently known between physical activity and presenteeism. DATA SOURCE: A search strategy was conducting in six scientific databases. STUDY INCLUSION AND EXCLUSION CRITERIA: Studies written in English about the relation between physical activity and presenteeism were considered for inclusion. DATA EXTRACTION: Data on definitions and measurement of presenteeism and physical activity were extracted. DATA SYNTHESIS: The data is categorized according to the understanding of presenteeism of the studies to give a better idea of how this phenomenon is studied in relation to physical activity. RESULTS: After screening 9773 titles and abstracts and 269 full-text articles, 57 unique articles fulfilled our eligibility criteria. The majority of the articles were published since 2010 and originated predominantly in the United States. Most studies (70%) define presenteeism as lost productivity due to health problems, according to the American line of research, whereas 19% of the studies define it as "working while ill" which refers to the European line of research. The studies that reflected the American school of thought tends to report more results that supported their hypothesis (i.e., that more physical activity is associated with less presenteeism). CONCLUSION: This review has highlighted the homogeneity in how presenteeism is conceptualized and measured in studies included in our sample. Research on physical activity and presenteeism should be expanded across various disciplines in social sciences to respond to the needs that many researchers have expressed to promote healthier organizations.
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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 | 0.029 | 0.118 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.027 | 0.023 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".