Understanding of plagiarism among North-African university hospital doctors (UHDs): A pilot study
Bibliographic record
Abstract
No previous North-African study has evaluated the UHDs understanding of plagiarism (UP). This descriptive study aimed to assess UP among Tunisian UHDs. UHDs were recruited via electronic mails sent to all the Tunisian UHDs through the national health networks and by convenience sampling via a questionnaire provided directly to some UHDs. The French survey, available from the Laval University website, includes 11 questions related to UP, with three-choice answers (yes/no/may be). One point was awarded for each correct answer. A total score lower than six corresponded to a low level of UP. 96 UHDs (69 females) responded to the survey either through emails (39.6%) or by filled in the paper (60.4%). The mean ±SD (95% confidence interval) score of UP was considered low at 5.4 ± 1.9 (5.0 to 5.8); 74% of the participants had a low UP. The UP score was significantly different between the categories of assistants and professors. Data comparison between subjective and objective assessments revealed that significant percentages of UHDs underestimated their low UP. This was more marked in the professors’ category. There was no significant correlation between the UP total score and the UHDs’ age or professional experience. To conclude, plagiarism is not well-known to North African UHDs. Abbreviations: MD: medical doctor; MSc: master of sciences; PhD: doctor of philosophy; r: Spearman correlation coefficient; SD: standard deviation; UHDs: university hospital doctors; UP: understanding of plagiarism; 95% CI: 95% confidence interval
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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 | Qualitative | high |
| gpt | MetaresearchResearch integrity Domain: Methods · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | high |
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".