Nurses’ perceptions regarding their own professionalism attributes to quality neonatal, infant and under-5 childcare
Bibliographic record
Abstract
Abstract Background Professional nurses are trained to provide quality care. Despite their skill, neonates, infants, and under-5 children mortality rates are high, and healthcare is challenged to reach sustainable development goal number 3 of healthy lives and to reduce the mortality rates. Methods This study employed a qualitative exploratory, descriptive design to explore and describe professional nurses’ professionalism attributes to provide quality care to neonates, infants, and under-5 children in the North West Province. 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 data analysis steps 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 with their respective themes and sub-themes. Conclusion Professional nurses are aware of their nursing professionalism attributes in quality of care in neonates, infants and under-5 children, ‘innovation and visionary’ attribute did not emerge, which should receive more attention to strengthen the quality of care. However, the attribute ‘professional reputation’ newly emerged in the South African context.
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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 | 0.010 | 0.021 |
| 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 | 0.004 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".