Why should we care about academic integrity in nursing students?
Bibliographic record
Abstract
Integrity, honesty, and respect are essential values for nursing students. Yet, in recent years the rise of breaches in academic integrity has become alarming. The era of increasing advances in the capabilities of smart technologies may be facilitating rather than deterring students from academic integrity breaches in their work and assessments. This issue raises questions for nurse academics on how they can best ensure that nursing students align their behavior with the expected nursing’s professional values, both during their study years and beyond. This discussion paper aims to examine contributing factors leading to breaches of academic integrity amongst nursing students and determine why we as nurse academics need to both remain alert to these factors, and vigilant about identifying and managing such breaches. Existing factors that may influence academic dishonesty are discussed concerning students; nurse academics; curriculum design; and the apparently growing cultural shift in ethical reasoning. With increased insight into these influencing factors, nurse academics need to take responsibility and prepare students to take on the highest standard of moral values to ensure safe and effective patient outcomes. We need to become more aware of and understand nursing students’ perspectives and adequately prepare our soon-to-become nurse graduates. We are charged with supporting, guiding, and teaching our students to develop skills within a culture of integrity. Ensuring an integrity activity smorgasbord in our practice can counteract the increased risk of academic dishonesty in our undergraduate nursing programs.
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 | 0.015 | 0.090 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.017 |
| Scholarly communication | 0.015 | 0.012 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.010 | 0.022 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".