Nurses’ perceptions regarding their own professionalism attributes to quality neonatal, infant and under-5 childcare
Bibliographic record
Abstract
BACKGROUND: Professional nurses are trained to provide quality care. Despite the professional nurses' acquired skill and professionalism attributes, the neonate, infant, and under-5-child mortality rates are high in sub-Saharan Africa. This health care report indicates that sub-Saharan Africa countries face a challenge in reaching the Sustainable Development Goal number 3 by the year 2030 (that is, ensuring healthy lives and reducing the mortality rates of children under 5). It has been reported that professionalism in nursing can improve the quality of care and positively change the health outcomes. METHODS: This study employed a qualitative exploratory, descriptive design to explore and describe professional nurses' own professionalism attributes to provide quality care to neonates, infants, and under-5 children in the North West province in South Africa. Eight naïve sketches of an all-inclusive sample of invited professional nurses (N = 25; n = 8) were received. The naïve sketch questions were based on the Registered Nurses' Association of Ontario's professionalism attributes. Tesch's eight steps of data-analysis were used, with an independent coder's assistance. RESULTS: The categories included (1) knowledge, (2) spirit of inquiry, (3) accountability, (4) autonomy, (5) advocacy, (6) collegiality and collaboration, (7) ethics and values, and (8) professional reputation, and each category generated sub-themes. CONCLUSION: Professional nurses are aware of their own professionalism attributes in quality of care of neonates, infants and under-5 children; the 'innovation and visionary' attribute did not emerge, which should receive more attention to strengthen quality care. However, a new attribute, 'professional reputation', reflecting a South African culture-orientated attribute, emerged from the data collected.
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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 | 0.009 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".