The impact of a journal club intervention on student perceptions and behaviours regarding academic dishonesty
Bibliographic record
Abstract
Background: There has been an increase in academic dishonesty among nursing students. Reasons for this increase are due in part to greater demands on nursing students and ready availability of technology that facilitates these behaviours. The purpose of this study was to determine if participation in a journal club would impact nursing students’ perceptions and behaviours regarding academic dishonesty. This issue needs attention because it impacts professional integrity. Methods: Researchers used a mixed-methods design. Seventy-nine nursing students from a baccalaureate program in the Midwestern United States participated in a journal club activity designed to stimulate conversation about academic dishonesty. Students were tested pre- and post-intervention. Transcripts of class discussion and written responses to case study questions were analyzed for identification of themes. Results: An unexpected finding was that reports of dishonesty increased after participation in the journal club intervention. The most common form described was copying the work of peers. Participants noted that both strong and weak students engage in academic dishonesty, and they believe that there are times when this behaviour is acceptable. They described the influence of personal circumstances and pressure to succeed on fostering the decision to engage in academic dishonesty. Participants described feeling frustrated and angry when they witnessed academic dishonesty among peers, but said they would not report a friend. Results suggest implications for faculties. Most participants believed faculties should clearly describe penalties for academic dishonesty and should strictly adhere to these penalties, although some believed that consequences should vary depending on the severity and number of episodes. Participants also identified actions faculties could implement to deter academic dishonesty. Conclusions: Students are under considerable pressure to succeed, which could lead to academic dishonesty; journal clubs could raise awareness of this issue. Educators cannot assume that students have the same definition of academic dishonesty as faculties; it is recommended that faculties state explicitly what acts are considered dishonest. Consequences for academic dishonesty should be clearly described verbally and in writing, and faculties should strictly adhere to stated penalties. Additionally, students have a role in promoting honesty in the classroom.
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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 | 0.007 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".