Antecedents and associations of sickness presenteeism and sickness absenteeism in nurses: A systematic review
Bibliographic record
Abstract
AIMS: This study comprehensively analysed and systemized the elements associated with nursing sickness presenteeism (SP) and sickness absenteeism (SA). BACKGROUND: Both behaviours represent a real challenge to nursing departments because they can increase costs, cause health care adverse events, and impact the quality of health care. DESIGN: The systematic review of cohort studies was designed to be consistent with the PRISMA guidelines. DATA SOURCES: PubMed, ProQuest, and Emerald were systematically searched for peer-reviewed articles published from the 1950s to December 2016. REVIEW METHODS: Cohort studies were included (12 SA and 1 SP) in the review if they examined the association between one or more exposures and SP and/or SA in nurses. The methodological quality of the included studies was assessed using the Newcastle-Ottawa Scale. RESULTS: Twenty-three antecedents were associated with SA and grouped as work and organizational, mental and physical health, and demographic; 3 antecedents were associated with SP (job demands, burnout, and exhaustion). Exhaustion (fatigue) and job demands were associated with SA and SP. Depersonalization was an outcome of SP over time. CONCLUSION: The ability to predict presenteeism and absenteeism in nursing is useful to constrain costs and ensure that quality care is delivered.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".