The impact of characteristics of nurses’ relationships with their supervisor, engagement and trust, on performance behaviours and intent to quit
Bibliographic record
Abstract
AIMS: The aim of this study was to investigate the influence of characteristics of nurses' relationship quality with their manager on engagement and trust, onto in-role or discretionary behaviours and intent to quit. BACKGROUND: Nurses having a good relationship with their manager are seen as important, yet the mechanisms of how such relationships are beneficial, or which aspects of the relationship are important, is less clear. Two possible mechanisms are through the nurse being more engaged in work, or through building their trust in their employer. In turn, engagement and trust may impact in-role and discretionary behaviours as well as staff retention. DESIGN: Cross-sectional. METHOD: An online survey in 2013 of 459 nurses across Australia. RESULTS: Structural analyses indicated that the affect dimension of relationship quality was negatively related to engagement, whereas contribution and respect were positively related to engagement. The affect and respect aspects were positively related to trust. Engagement positively related to discretionary and in-role behaviours. Engagement and trust were negatively related to quit intention, as was the loyalty dimension of the nurses' relationship with their supervisor. However, perceptions of variability in their team's relationship quality with their leader was negatively related to trust and positively related to intent to quit. CONCLUSIONS: Nurse managers with a nuanced understanding of social exchange at work are likely to maintain more engaged, well-performing and stable nursing teams. In particular, a willingness by the supervisor to come to their nurses' defence and having a consistent standard of relationship quality across their nurses is likely to improve nurse retention.
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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 | 0.000 | 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".