ASSESSMENT OF PROFESSIONALISM IN NURSING AND FACTORS ASSOCIATED AMONG NURSES WORKING IN ARSI ZONE PUBLIC HOSPITALS, OROMIA, ETHIOPIA, 2018
Bibliographic record
Abstract
Abstract Background Professionals are defined in the context of a particular body of knowledge which is obtained through formal education, expanded level of skills, type of certification proving their entry into the profession; a set of behavioral norms called professionalism and attitudes representing high levels of commitment to and identification with a specific profession. Several factor affecting the development of the nursing profession. Recognizing and determining such factors can be the first step to move towards the professionalization of nursing. The objective of this study was to assess professionalism in nursing and factors associated among nurses working in Arsi zone, Public Hospitals, Oromia, Ethiopia 2018. Methods This study used an Institutional based cross sectional study design. Self-administered structured questionnaire adapted from RNAO (Registered Nurses’ association of Ontario) guideline, was used to measure the level of professionalism. The sample was 420 nurses from the six Public Hospitals of Arsi Zone, Oromia, Ethiopia. Data was analyzed using SPSS 20.0. Both bivariate and multivariate analysis were carried out to identify associations. Odds ratio was calculated for related factors with 95% confidence interval (CI). A p-value < 0.05 was considered to be statistically significant. Result Out of 420 Nurses working in six public Hospitals, 380 responded to the questionnaire, making the response rate of 90.5%. In current study level of professionalism was high among nurses (n=380) with highest percentages on accountability, advocacy, and ethics. Gender (AOR=2.489, 95% CI=1.540-4.023), nursing is indispensable (AOR=1.760, 95% CI=1.104-2.806), job satisfaction (AOR= 1.844, 95% CI = 1.143-2.975) and having up to dated training (AOR= 1.809, 95%CI=1.071-3.055 were significantly associated with overall professionalism level. Conclusion Nurses working in public hospitals of Arsi zone have relatively had better professionalism level. Gender, nursing is indispensable, job satisfaction, presence of up-to-dated trainings were found significantly associated with professionalism in nursing. Human resource personnel and CEO’s of respective hospitals should develop various training programs for nurses and provide encouraging environments for obtaining better qualities in attributes of professionalism.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| 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".