“My hands are tied”: Nurses’ perception of organizational culture in Kenyan private hospitals
Bibliographic record
Abstract
It is estimated that by 2030 the global shortage of nurses and midwives will be 7.6 million, with African countries among the most adversely affected. Within this context, it is important to understand the specific organizational factors that contribute to registered nurses (RNs)’ decisions to remain or leave their workplaces in East Africa. The aim of this study was to commence exploration of these factors by exploring nurses’ perceptions of organizational culture of selected private hospitals in Kenya. A small-scale exploratory qualitative approach was employed, with eight nurses undertaking semi-structured interviews. There were five female and three male nurses. All participants were RNs; two held a bachelor’s degree in nursing and the rest held diplomas in nursing. The thematic analysis revealed four major themes and nine sub-themes. The major themes included: restrictive work environment, top-down leadership, normalizing the abnormal in team dynamics and professionalism, and ethical concerns. These findings suggest an urgent need for Kenyan private hospital administrators to create a hospital work environment that provides more autonomy for nurses. There is a need for inclusive leadership styles that target hospital organizational structures and processes in order to address nursing workforce team dynamics. A merit-based salary and progressive reward systems are recommended to empower nurses to remain in the workforce.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".