Effect of a supportive leadership initiative on nurse leaders’ work performance in Malawi: A presentation of qualitative findings
Bibliographic record
Abstract
Background: Nurses with inadequate clinical experience assume leadership positions as first-line managers in hospitals in Malawi due to shortage of Registered Nurses. It is presumed with support from senior nurse leaders, inexperienced nurses can lead successfully. Research suggests Supportive Leadership (SL) can positively influence performance. The study examined the effect of SL (mentoring, relationship building, team working, and positive work environment) on performance of Inexperienced Nurse Leaders (INLs) in the Central West Quality Satellite Zone Hospitals of Malawi. Methods: A convergent mixed methods design was used, with qualitative findings being presented. The sample was selected using purposive sampling. Data was collected through in-depth interviews with 10 Senior Nursing Officers (SNOs) and 10 INLs and was analysed using Collaizi’s thematic method. Ethical approval was granted by the National Commission for Science and Technology in Malawi. Results: SL promoted the performance of INLs. Mentoring empowered the INLs to lead though it was done inconsistently. The INLs acknowledged the presence of work relationships and collaborative healthcare teams in the wards/units. The work relationships and teams eased work and the INLs felt supported. The work environment was positive in some wards and non-conducive in others. The main challenges were a shortage of nurses, financial and material resources. Conclusion: The research results have shown the importance of SL of SNOs in the CWQSZ hospitals of Malawi despite the existing shortage of nurses, material, and financial resources. The leadership style promoted the performance of INLs. The results contribute to the existing limited body of knowledge of SL in nursing.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".