Nurses empowerment at primary health care centers and its relation with quality of work life
Bibliographic record
Abstract
Background and objective: Nurses in primary health care are a considerable group of professionals working in the health sector and an adequate quality of working life will empower them to provide favorable quality care to their clients. Further, a better quality of working life can keep the employees focused and support them to strive effectively towards the organization’s vision. The aim of the study was to explore the relationship between nurses' empowerment and quality of work life at primary health care centers in Assiut City.Methods: This study was conducted in a descriptive correlated manner; the population consisted of the Assiut city primary health care centers nursing staff (n = 85). Self-administered questionnaire consisted of three parts: 1st part-Personal characteristics data questionnaire, 2nd part-Work Empowerment Promoting Factors Scale, and 3rd part: Quality of Nurses' Work Life questionnaire.Results: There were significant negative correlations between quality of work life and empowerment factors among studied nurses.Conclusions and recommendations: Workshops on the quality of work life skills for nurses and nurse managers should be done periodically, nurse managers in MCH centers should provide nurses by sufficient information, guide and resources, Nurses in primary health centers have to remain allowed to participate in decision making process to empower them, as like properly as growing theirs effect on autonomy, and First line nurse managers should periodically have nursing group meeting to verbalized, vitalize and support peer and social interaction.
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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 | 0.007 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".