Perceptions of Nurses on Patient Outcomes Related to Nursing Shortage and Retention Strategies at a Public Hospital in the Coastal Region of Tanzania
Bibliographic record
Abstract
Background: There is little disagreement that the shortage of nurses affects patients’ outcomes globally. However, within the low and middle income country setting, there is minimal known about the perceptions of nurses on nursing shortages impact the health outcomes of their patients and what recruitment and retention strategies might be appropriate to address some of these challenges. This study explored the perceptions of nurses on the health outcomes of patient related to shortage of registered nurses and the strategies to retain nurses at a public hospital in Tanzania. Method: This qualitative descriptive study used semi-structured in-depth interviews with a select group of nurses in a large public hospital. Findings: Through an iterative coding process, a series of categories were derived which yielded three major themes—factors contributing to nursing shortage; compromised quality of care; and recruitment and retention strategies. Conclusion: A shortage of nurses affects the health outcomes of patients as it potentially hinders timely accomplishment of the optimal nursing. Efforts need to be proactive in recognizing the reasons for nursing shortages which are rooted in individual, institutional (agency), and organizational (systemic) issues. Within the LMIC context, such as where this study was conducted, it became apparent that the nurses wanted acknowledgement and opportunities to work collaboratively towards the resolution of workload issues for the benefit of the patients.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 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".