Relationship between organizational trust and organizational citizenship behaviors: Staff nurses’ perspective
Bibliographic record
Abstract
Background and objective: Individuals, who work in an organization where there is a high level of trust, perceive themselves as an important and valued part of this organization. With more enthusiasm, they come to work and are happier with their jobs. Thus, staff nurses’ organizational trust, which forms the basis of intra-organizational relationships, can affect their performance and help them to demonstrate organizational citizenship behaviors. The aim of this study was to assess organizational trust and organizational citizenship behaviors from staff nurses’ perspective. Also, to investigate the relationship between organizational trust and organizational citizenship behaviors at Alexandria Main University Hospital.Methods: A descriptive correlational design was utilized with convenient sample of staff nurses (n = 352) including staff nurses who works in units of medical care (n = 90), in units of surgical care (n = 120), and in critical care units (n = 142). Two tools were used to measure the study variables: Tool I: Organizational Trust Inventory (OTI). Tool II: Organizational Citizenship Behavior Scale (OCBS).Results: The highest mean score of organizational trust was related to trust in managers, while, the lowest mean score was related to trust in organization. The highest mean score of organizational citizenship behaviors was related to conscientiousness, while the lowest mean score was related to sportsmanship.Conclusions and recommendations: Staff nurses perceived high organizational trust and moderate organizational citizenship behaviors. There is a strong positive high significant correlation between overall organizational trust and overall organizational citizenship behaviors as perceived by staff nurses. Continuous periodic training programs should be given for staff nurses in different healthcare units, to increase their awareness about organizational trust and how to demonstrate organizational citizenship behaviors.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".