Bibliographic record
Abstract
AIM: The purpose of the present study was to determine the accuracy of nurses' self-reports of absence by examining: (1) the correlation, intra-class correlation, and Cronbach's alpha for self-reported absence and absence as reported in organizational records, (2) difference in central tendency for the two measures of absence and (3) the percentage of nurses who underestimate their absence. BACKGROUND: Research on nurses' absenteeism has often relied on self-reports of absence. However, nurses may not be aware of their actual absenteeism, or they may underestimate it. METHOD: Self-reported absence from questionnaires completed by 215 Canadian nurses was compared with their absence from organizational records. RESULTS: There is a strong positive correlation, a strong intra-class correlation and Cronbach's alpha for the two measures of absence. However, there is a difference in central tendency that is related to the majority of nurses in this study (51.1%) underestimating their days absent from work. CONCLUSIONS: Research examining the predictors of absence may consider measuring absence with self-reports. Nevertheless, nurses demonstrated a bias to underestimate their absence. IMPLICATIONS FOR NURSING MANAGEMENT: Feedback interventions to reduce absenteeism can be developed to include providing nurses with accurate information about their absence.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".