Combating Scientific Misconduct: The Role of Focused Workshops in Changing Attitudes Towards Plagiarism
Bibliographic record
Abstract
Introduction Scientific misconduct is a global issue. There is low awareness among health professionals regarding plagiarism, particularly in developing countries, including Pakistan. There is no formal training in the ethical conduct of research or writing for under- and post-graduate students in the majority of medical schools in Pakistan. Internet access to published literature has made plagiarism easy. The aim of this study was to document the effectiveness of focused workshops on reducing scientific misconduct as measured using a modified version of the attitude towards plagiarism questionnaire (ATPQ) assessment tool. Materials and methods A cross-sectional study was conducted with participants of workshops on scientific misconduct. Demographic data were recorded. A modified ATPQ was used as a pre- and post-test for workshop participants. Data were entered in SPSS v20 (IBM< Armonk, NY, US). Frequencies and descriptive statistics were analyzed. An independent sample t-test was run to analyze differences in mean scores on pre-workshop ATPQ and differences in mean scores on post-test scores. Results There were 38 males and 42 females (mean age: 26.2 years) who participated in the workshops and completed the pre- and post-assessments. Most (59; 73.75%) were final-year medical students. One-third (33.8%) of the respondents had neither attended workshops related to ethics in medical research nor published manuscripts in medical journals (32.5%). More than half (55%) admitted witnessing unethical practices in research. There was a significant improvement in attitudes toward plagiarism after attending the workshop (mean difference = 7.18 (6.2), t = 10.32, P < .001). Conclusions Focused workshops on how to detect and avoid scientific misconduct can help increase knowledge and improve attitudes towards plagiarism, as assessed by the modified ATPQ. Students, residents, and faculty members must be trained to conduct ethical medical research and avoid all forms of scientific misconduct.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Research integrity Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | Research integrity Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | medium |
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 | 0.008 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".