Predictors of nurse absenteeism in hospitals: a systematic review
Bibliographic record
Abstract
AIM: This study aimed to identify and examine predictors of short-term absences of staff nurses working in hospital settings reported in the research literature. BACKGROUND: Front-line staff nurse absenteeism contributes to discontinuity of patient care, decreased staff morale and is costly to healthcare. EVALUATION: A systematic review of studies from 1986 to 2006, obtained through electronic searches of 10 online databases led to inclusion of 16 peer-reviewed research articles. Seventy potential predictors of absenteeism were examined and analysed using content analysis. KEY ISSUE: Our findings showed that individual 'nurses' prior attendance records', 'work attitudes' (job satisfaction, organizational commitment and work/job involvement) and 'retention factors' reduced nurse absenteeism, whereas 'burnout' and 'job stress' increased absenteeism. Remaining factors examined in the literature did not significantly predict nurse absenteeism. CONCLUSIONS: Reasons underlying absenteeism among staff nurses are still poorly understood. Lack of robust theory about nursing absenteeism may underlie the inconsistent results found in this review. Further theory development and research is required to explore the determinants of short-term absenteeism of nurses in acute care hospitals. IMPLICATIONS FOR NURSING MANAGEMENT: Work environment factors that increase nurses' job satisfaction, and reduce burnout and job stress need to be considered in managing staff nurse 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 | 0.008 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.004 |
| Bibliometrics | 0.009 | 0.012 |
| 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".