The relationship between resilience and empowering leader behaviour of nurse managers in the mining healthcare sector
Why this work is in the frame
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Bibliographic record
Abstract
BACKGROUND: The South African mining healthcare sector faces injuries, illnesses including HIV and AIDS and high staff turnover rates. In this sector, nurse managers should create an optimal environment for providing nursing care by motivating, influencing and empowering nurses. OBJECTIVES: This study aimed to investigate the relationship between nurse managers' resilience and empowering leader behaviour in this sector. METHOD: The study employed a quantitative, descriptive and correlational design. The research population comprised 31 nurse managers, 101 professional nurses, 79 enrolled nurses and 79 enrolled nursing auxiliaries who participated in the study. Two questionnaires were used as data collection methods, namely Wagnild and Young's Resilience Scale Questionnaire to investigate the resilience of nurse managers and the Empowering Leadership Questionnaire to measure empowering leader behaviour of the nurses supervised by a particular nurse manager. RESULTS: Out of 31 nurse managers, 8 had a low level, 19 had a moderate level and 4 had a high level of resilience. According to Hoteling's t-test the nurse managers in the low resilience group displayed lower empowering leader behaviour as perceived by their team members than those in the high resilience group in terms of the five factors included in the Empowerment Leadership Questionnaire. CONCLUSION: Respondents with high resilience scores tended to have higher leader empowering behaviour.Recommendations include the strengthening of nurse managers' resilience through workshops and reflection practices, debriefing and performance feedback sessions.
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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 | 0.000 | 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 it