Presenteeism – a common phenomenon in the studypopulation of nurses
Bibliographic record
Abstract
Introduction: Presenteeism is defined as an employee's attending work despite illness.The phenomenon has become more widespread in the health protection sector, especially among nurses.In addition, the researchers emphasize that this phenomenon has a huge impact on the economic burden resulting from the employee's presence at work despite their illness, and it contributes to an increase in the number of medical errors.It seems justified to highlight the problem and define the factors determining such an attitude among nurses.The aim of the study was to indicate the predictors of operating room nurses' presence at work despite disease symptoms and classification of the symptoms.In addition, the attitudes of people who come to work ill were compared with those who stay at home during illness, to observe the differences. Material and methods:The study was carried out in 2021 and covered 900 working nurses taking the state examination in operating room nursing.A total of 861 surveys were analysed.The study authors used an original questionnaire about ill employees coming to work (being on duty) and the most common symptoms accompanying work when ill. Results: There were many factors that contributed statistically significantly to the attitude of presenteeism; they included the sense of responsibility towards workmates (p = 0.000, χ 2 = 16.86) and the employer (p = 0.000, χ 2 = 14.49) or concerns about stable employment (p = 0.016, χ 2 = 5.89).A lack of statistical significance for the sense of responsibility towards the patient was an interesting observation.Moreover, the respondents were aware that coming to work when ill affects the quality of work and contributes to a higher risk of committing an error.Conclusions: Even though presenteeism is deeply rooted in a nurse's job culture, the nursing staff, employers, and decision-makers in the health protection sector should be aware of its negative consequences.
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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 | 0.012 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".