Hospital Nurses??? Perceptions of Respect and Organizational Justice
Bibliographic record
Abstract
OBJECTIVE: To test an exploratory model of the antecedents and consequences of nurses' perceptions of respect in hospitals. BACKGROUND: Although nurses in hospital settings often state that they do not receive the respect they deserve for their contribution to patient care, there is little empirical research on this phenomenon. Interactional organizational justice theory framed the analysis. METHODS: A random sample of 285 staff nurses from Ontario teaching hospitals completed measures of interactional justice, structural empowerment, perceived respect, work pressures, emotional exhaustion, and work effectiveness. RESULTS: More than half of the nurses felt that managers did not show concern or deal with them in a sensitive and truthful manner regarding decisions affecting their jobs. The strongest predictors of perceptions of respect were interactional organizational justice, followed by structural empowerment and job stress resulting from lack of recognition, poor interpersonal relationships and heavy workload. Consequences of nurses' feelings of respect included greater job satisfaction, trust in management, and lower emotional exhaustion, as well as higher nurse ratings of quality of care and perceived staffing adequacy. CONCLUSIONS: A positive organizational environment increases nurses' perceptions of respect, resulting in positive outcomes for both the nurse and the organization.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".