Improving knowledge, skills, and attitudes of the nursing faculty members and postgraduate students towards plagiarism in academic writing
Bibliographic record
Abstract
Background and aim: Plagiarism is the use of concepts, words, manuscript and data without acknowledgment of the original source. It has become a worldwide problem, and a contentious matter in university education and research. The study aimed to improve knowledge, skills, and attitudes of the nursing faculty members and postgraduate students towards plagiarism in academic writing.Methods: A quasi-experimental (pre-post) design. Setting: The study was accomplished at the Nursing Faculty in Mansoura University. Sample: Convenient sample was used included 195 participants (100 nursing faculty members and 95 postgraduate students) of all nursing specialties who attended and completed the educational & training workshop. Tools: A structured Interviewing Schedule, Attitudes toward Plagiarism Scale and Plagiarism Scenario-Based Questionnaire.Results: The average score of the participants' knowledge and their practical scenario-solving scores about plagiarism were significantly increased after the training workshop compared to their levels before it. In addition, the average scores of the positive attitude, subjective norms and total attitudes score towards plagiarism were significantly decreased, while the average score of the negative attitude significantly increased after the training workshop in comparison to before it. Moreover, there was a statistically significant positive, moderate correlation between the participants’ knowledge score about plagiarism and their practical scenario-solving scores (r = 0.346, p ≤ .001). In addition, there was a significant positive mild correlation between the total knowledge score and the negative attitude of the studied sample towards plagiarism (r = 0.254, p ≤ .001).Conclusions: It was evident that there was a statistically significant improvement in knowledge and skills of the nursing faculty members and postgraduate students with a significant change in their attitudes towards plagiarism after implementation of the workshop. Recommendations: Providing continuing educational and training programs for the newly faculty members and researchers to improve their scientific writing skills, and research ethics and for highlighting plagiarism and its consequences.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".