Steering resilience in nursing practice: Examining the impact of digital innovations and enhanced emotional training on nurse competencies
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it