Discovering presence as part of nurse educators’ role modelling at a public nursing college in the North West province
Bibliographic record
Abstract
BACKGROUND: Nursing students learn the science and art of nursing, including presence, from classroom content, using skills in practice, or by watching an experienced nurse interact with a patient. Nursing education must be designed so that nursing students can construct the art and science of nursing practice. Nursing students must be educated to be sound practitioners in the 'being' of nursing practice. Nurse educators modelling presence to nursing students will improve the quality of patient care during clinical training and throughout their professional role. AIM: To explore and describe nurse educators' role modelling of presence to nursing students. SETTING: This study was conducted at a public nursing college in the North West province. METHODS: A qualitative, ethnographic study was conducted. Purposive sampling was used. Four nurse educators participated in the study and data saturation was reached. Data were collected through shadowing and informal reflective conversations over a period of 8 days. RESULTS: The following relationships emerged: nurse educators model 'being professional', 'being facilitating, nurturing, caring and compassionate, encouraging, and motivating', and 'being purposeful in their nursing education approach'. CONCLUSION: Participants role modelled presence to nursing students despite daily challenges in their work. CONTRIBUTION: Creating awareness of how nurse educators can model presence despite daily challenges in their work will influence and motivate nursing students to develop presence skills. This will have a positive impact on managing patients in practice. Recommendations can guide nursing education, policy development and future research to strengthen nurse educators modelling presence.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.016 | 0.005 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".