Effects of absenteeism feedback and goal-setting interventions on nurses’ fairness perceptions, discomfort feelings and absenteeism
Bibliographic record
Abstract
AIM: A longitudinal field experiment was conducted to test the effects of absenteeism feedback and goal-setting interventions on nurses' (1) fairness perceptions, (2) discomfort feelings and (3) absenteeism. Nurses' obstacles to reducing absenteeism were also explored. BACKGROUND: Absenteeism is a significant issue in health care and there is a need to avoid interventions that are seen to be negative, punitive or lead to sick nurses coming to work. METHOD: Sixty-nine nurses working in a hospital in Eastern Canada received either: (1) absenteeism feedback with individual goal-setting, (2) absenteeism feedback with group goal-setting, or (3) no intervention, and were asked questions about how they could reduce their absenteeism. RESULTS: There was a significant decrease in the total number of days absent but no decrease in absent episodes, and a significant effect on fairness perceptions and discomfort feelings for the nurses in the absenteeism feedback conditions. Six categories of obstacles to reducing absenteeism were identified. CONCLUSIONS: The interventions made nurses feel their absence rate was less fair and to experience greater feelings of discomfort. IMPLICATIONS FOR NURSING MANAGEMENT: The study's interventions may lead to a reduction in absence without the negative outcomes of a harsh absenteeism policy.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".