The relationship between resilience and empowering leader behaviour of nurse managers in the mining healthcare sector
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 |
| 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".