Effective educator–student relationships in nursing education to strengthen nursing students’ resilience
Bibliographic record
Abstract
BACKGROUND: Little research has been conducted in private nursing schools with regard to the educator-student relationship to strengthen the resilience of nursing students and to improve the educator-student relationship. An effective educator-student relationship is a key factor to ensure a positive learning climate where learning can take place and resilience can be strengthened. PURPOSE: The purpose was to explore and describe nursing students' view on the basic elements required for an effective educator-student relationship to strengthen their resilience and the educator-student relationship. METHOD: This study followed an explorative, descriptive and contextual qualitative design in a private nursing education institution in the North West Province. Purposive sampling was used. The sample consisted of 40 enrolled nursing auxiliary students. The World Café Method was used to collect data, which were analysed by means of content analysis. RESULTS: The following five main themes were identified and included: (1) teaching-learning environment, (2) educator-student interaction, (3) educator qualities, (4) staying resilient and (5) strategies to strengthen resilience. CONCLUSION: Students need a caring and supportive environment; interaction that is constructive, acknowledges human rights and makes use of appropriate non-verbal communication. The educator must display qualities such as love and care, respect, responsibility, morality, patience, being open to new ideas, motivation, willingness to 'go the extra mile' and punctuality. Students reported on various ways how they manage to stay resilient. It thus seems that basic elements required in an effective educator-student relationship to strengthen the resilience of students include the environment, interaction, educator and student's qualities and resilience.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".