Identifying self-presentation components among nursing students with unsafe clinical practice: a qualitative study
Bibliographic record
Abstract
BACKGROUND: Maintaining patient safety is a practical standard that is a priority in nursing education. One of the main roles of clinical instructors is to evaluate students and identify if students exhibit unsafe clinical practice early to support their remediation. This study was conducted to identify self-presentation components among nursing students with unsafe clinical practice. METHODS: This qualitative study was conducted with 18 faculty members, nursing students, and supervisors of medical centers. Data collection was done through purposive sampling and semi-structured interviews. Data analysis was done using conventional qualitative content analysis using MAXQDA10 software. RESULTS: One main category labelled self-presentation emerged from the data along with three subcategories of defensive/protective behaviors, assertive behaviors, and aggressive behaviors. CONCLUSION: In various clinical situations, students use defensive, assertive, and aggressive tactics to maintain their professional identity and present a positive image of themselves when they make a mistake or predict that they will be evaluated on their performance. Therefore, it seems that the first vital step to preventing unsafe behaviors and reporting medical errors is to create appropriate structures for identification, learning, guidance, and evaluation based on progress and fostering a growth mindset among students and clinical educators.
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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 | 0.012 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".