Professionalism and associated factors among nurses working in Hawassa city public hospital, Sidama, Ethiopia
Bibliographic record
Abstract
Background The foundation of the global healthcare system is nurses, and professionalism in nursing is a basic idea that helps patients, organizations, and people. Studies that have been published in Ethiopia, though, are limited, out-of-date, and poorly documented, especially when it comes to the study setting. Because of this, this study aimed to close a knowledge gap on the level of professionalism in public hospitals in Sidama, Ethiopia. Objective This study aimed to assess professionalism and associated factors among nurses working in Hawassa city public hospitals, Hawassa, Ethiopia. Methods An institutional-based cross-sectional study was conducted among nurses working in Hawassa city public hospital from June to July 2022. A computer-generated simple random sampling technique was used to select 413 study participants. The level of professionalism was assessed through a self-administered questionnaire, using the guidelines of the Registered Nurses Association of Ontario. All the loaded data using Epi-data version 4.6 were exported to a statistical package for social science. An ordinal logistic regression analysis was used to identify the associations between the outcome and predictor variables. The statistical significance of the factors influencing the outcome variable was declared in multivariate logistic regression analysis using an adjusted odds ratio at a 95% confidence interval with a p-value <0.05. Results A total of 405 nurses participated in the study, with a response rate of 98%. Of the total participants, more than half were females (55.3%). The level of professionalism was found to a moderate level. There was a strong link between completing their degree in a governmental institution, being part of a professional organization, serving for several years, and having a BSc or above qualification with a moderate level of professionalism. Conclusion We found a moderate level of professionalism among nurses working in the study setting. This suggests that the Regional Health Bureau should collaborate with other responsible bodies to develop various opportunities for nursing staff to increase their professionalism. The minister of health should be focused on private college nurses, nurses lacking the association, and the qualification of the profession.
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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 | 0.001 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".