Academic integrity in first-year nursing students: An intergrative review
Bibliographic record
Abstract
Introduction: Studies relating specifically to first-year nursing students’ breaches of academic integrity are not readily identified within the literature. While work has, over the years reported on breaches in academic integrity, it has done so with many disciplines and varied student cohorts. Nevertheless, those that specifically relate to first-year nursing students are sparse. First-year undergraduate nursing students are generally included with the entire student nurse cohort, and not necessarily identified independently. Therefore, the integrative review aimed to discover how or if the incidence of plagiarism/cheating, specifically within first-year undergraduate nursing students, was addressed.Methods: An integrative review of the literature, incorporating quantitative and qualitative literature was undertaken between January 2021 – April 2021 using electronic data bases.Results: Ten studies met the inclusion criteria. From the review, six main themes were extrapolated, (1) blurred boundaries of caring; (2) navigating the ethical highway; (3) factors influencing academic misconduct (4) early intervention (5) academic integrity policies and procedures and (6) inconsistencies amongst academics.Conclusions: The integrative review has identified a shortfall in research relating to how academic integrity has been addressed in first-year nursing students.
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 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 | 0.018 | 0.078 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.018 | 0.016 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 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".