How do I stand compared to agency workers? Justice perceptions and employees' counterproductive work behaviours
Bibliographic record
Abstract
AIMS: To test the influence of comparative procedural justice on the counterproductive behaviours of permanent nurses and care attendants who work with agency workers, and explore whether the perceived climate of competition between permanent and agency workers alters this relationship. BACKGROUND: Despite steady reliance on agency workers in the health care sector, there is a dearth of research on the reactions of permanent employees who may respond negatively to the presence of this external workforce. METHODS: Questionnaires were distributed to employees of three long-term care facilities and their supervisors. Hypotheses were tested using moderated mediation analyses on a sample of 232 employee-supervisor dyads. RESULTS: Comparative procedural justice was indirectly related to counterproductive behaviours via employees' organization-based self-esteem. This relationship was weaker when perceived climate of competition was high. CONCLUSION: Promoting high levels of comparative procedural justice among permanent employees, rather than status differences with agency employees, should avert behaviours that could harm organizational functioning and therefore patient care. IMPLICATIONS FOR NURSING MANAGEMENT: We discuss the leadership challenges to support neutral treatment and avoid the development of a competitive climate between permanent and agency workers (e.g., politics based on respect and collaboration, positive leadership).
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".