Hallmarks of nursing students exhibiting unsafe clinical practices: a qualitative study
Bibliographic record
Abstract
BACKGROUND: Maintaining and promoting patient safety is a shared responsibility among all participants in the health care system. Educators are required to balance patients' rights to receive safe care and create a suitable and safe environment for nursing students to learn. Therefore, early identification of students with unsafe clinical practice and intervention may be important measures for improving patient safety. Therefore, the present study was conducted with the aim of identifying the main hallmarks of nursing students with unsafe clinical practice. METHODS: This qualitative study was conducted with 19 faculty members, nursing students, and supervisors of medical centers. Data collection was performed through purposive sampling and semi structured interviews. Data analysis was performed via conventional qualitative content analysis via MAXQDA10 software. RESULTS: The results of the study led to the identification of 2 main categories, "Underdeveloped knowledge and cognitive capacity" and "Underdeveloped personal-professional capacity", and 6 and 4 subcategories, respectively, as the main hallmarks for identifying students with unsafe clinical practice. CONCLUSION: The findings of this qualitative study expand our understanding of the hallmarks of nursing students with unsafe clinical practice. Undergraduate nursing students with unsafe clinical practice may not have acquired sufficient development and progress in terms of knowledge, skills, and personal-professional characteristics or may not be able to demonstrate them in their practices. Nursing schools must ensure that students have the necessary knowledge, skills, competencies, and personal-professional characteristics to participate in clinical training programs. It is recommended that students with unsafe clinical practices be identified early so that patient safety is maintained and that students are supported in order to correct their weaknesses and improve.
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.
How this classification was reachedexpand
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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".