Assessment of professional nursing governance and hospital magnet components at Alexandria Medical Research Institute, Egypt
Bibliographic record
Abstract
Background and objective: In the context of a rapidly evolving health care system, health care institutions strive to set a path towards an excellent professional practice environment. Since improving clinical nurse work environments is a major issue faced by nurse executives and administrators, they become challenged to establish nursing governance models, and leadership practices so that clinical nurses can engage in the work processes and relationships that are empirically linked to quality patient outcomes. The main aim of this study was to assess the current status of professional nursing governance and hospital magnet components at Alexandria Medical Research Institute, Egypt.Methods: A descriptive research design was conducted at Alexandria Medical Research Institute hospital, using a convenience sample (N = 220) that composed of two groups including; all hospital medical administrators (n = 10) and hospital nursing workforce (n = 210). Index of Professional Nursing Governance Questionnaire (IPNGQ) and Magnet Hospital Forces Interview were proved valid and reliable to measure study variables.Results: The overall mean score of professional nursing governance was (187.59 ± 63.74) reflected that staff nurses practice the first level of nursing shared governance (primarily nursing management who take the decision with some staff input). In addition, both medical administrators and nursing staff identified the hospital has a good structure, nursing leadership practices that support shared governance and magnet recognition. Structural equation model and correlation analysis revealed a positive association between overall professional nursing governance and hospital magnet components (p < .05).Conclusions and recommendations: The study emphasized the hospital administrators’ important role for providing supportive organizational structures and leadership practices for increasing participation of nursing staff in work design, problem-solving, conflict resolution, committees and organizational decision-making as “key ingredients to a successful organization” in turn, lead to a healthy and magnet-like work environment. Training programs for nurses’ professional development are recommended which enhance and increases their autonomy and empowerment.
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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 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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".