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 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 | 0.008 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".