Steering resilience in nursing practice: Examining the impact of digital innovations and enhanced emotional training on nurse competencies
Bibliographic record
Abstract
The phenomenal development of healthcare practice in the past few decades has reinforced the view that technology could potentially be the third healing triad element. This study, using data from Australia and the United Kingdom, explores resilience in nursing education through the lens of emerging digital technologies and enhanced emotional training. The study employed a mixed-method approach. A pretest-posttest was used to collect data from 54 nursing students during the lectures and tutorials, whilst the qualitative consisted of interviews with 20 health professionals, including nurse teachers and doctors. We found that students’ confidence in mental health nursing practice improved substantially after mental health placement. Besides, the effectiveness of the training offered was not compromised by variances in the demographic groups (e.g. age and gender) amongst the participants. The interview findings revealed that nurses could develop more outstanding modern capabilities with exposure to increasingly used technologies in the healthcare sector; thus, AI and digital technology and health-related engineering equipment can help reduce stress in the profession as machines become critical aid. Technology is, thus, not a threat but a necessary complement that can upskill nurses for contemporary practice.
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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 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.005 |
| 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".