Refining Bronfenbrenner's Model to Develop and Promote the Nursing Specialist Role in Saudi Arabia
Bibliographic record
Abstract
Almost a quarter of children in Kingdom of Saudi Arabia (KSA) are diagnosed with asthma, and it is the third most common reason for hospital admissions, representing a major public health challenge. This article review is a chapter from PhD thesis that identified the perspective of health professional and patients about the nursing role during pediatric asthma management based Burawoy’s extended case method. A qualitative paradigm was deemed to be most appropriate to fully grasp the meanings that the participants attach to the phenomenon of childhood asthma management. Then, this review described a a new Nursing Role Development Model as a way of conceptualising the nursing role generally, in order to recognize the importance of multi-level environments as well as interactions between the levels as key factors influencing development which may applied to the field in Saudi Arabia. The impact of the clinical nurse specialist enhances patient care and promotes professional nursing practice. This article provides a review of the asthma management and its challenges, a description of multifactor such as professional power, policy, culture and gender roles on stakeholders’ perceptions of nurses in general and asthma nurse specialists in particular, and a discussion of opportunities and potential threats to future growth of the clinical nurse specialist role.
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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 | 0.007 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".