Academic and clinical integrity: undergraduate nursing students’ perceptions
Bibliographic record
Abstract
Abstract Background Despite education on academic integrity, there are still instances where nursing students breach academic integrity principles. Nursing students who breach academic integrity principles in the classroom might engage in dishonest behaviors in the clinical setting. Globally, there are limited studies on nursing students’ perceptions of academic and clinical integrity. Furthermore, there are no studies on the perceptions of nursing students in Qatar on academic and clinical integrity. Purpose To explore the perceptions of undergraduate nursing students in Qatar on academic and clinical integrity. Methods A descriptive qualitative inquiry using face-face and online interviews. Results Four major themes and sub-themes emerged: (1) Definitions of Academic and Clinical Integrity; (2) Facilitators of Academic and Clinical Integrity with sub-themes of institutional support, ethical practice, and professional practice; (3) Barriers to Academic and Clinical Practice with sub-themes of peer influence, time influence, and fear of mistakes; (4) Improvements to academic and clinical integrity at the institutional level. Conclusion Participants in this study viewed academic and clinical integrity from a positive lens. Identified facilitators and barriers to academic and clinical integrity from a nursing student perspective in Qatar align with global nursing student perspectives.
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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 | 0.018 |
| 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 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".