Knowledge of misconduct amid North-African post-graduate dental students: A cross sectional study
Bibliographic record
Abstract
Background Misconduct in the academic community remains poorly understood among post-graduate dental students (PGDSs) in North Africa. Data on the knowledge of misconduct (KoM) level in this population is lacking. This brief report assessed KoM of Tunisian PGDSs’. Methods A cross-sectional study was conducted at the Faculty of Dental Medicine of Monastir, involving 147 PGDSs registered in 2022. Students were recruited via email invitations and convenience sampling at a medical congress. A French survey ( i.e. ; Laval University quiz) with 11 questions on KoM, offering three-choice answers (yes/no/maybe) was administered. Each correct answer received one point, and a total score below six indicated a low-level of KoM. Results The mean±SD KoM score of the 106 students who accepted to participate in the study was 4.4±1.8, indicating a low-level of KoM. The majority of PGDSs (85.85%) demonstrated a low-level of KoM. A comparison between subjective and objective assessments of KoM levels revealed that a significant percentage of PGDSs underestimated their knowledge (62.26% vs. 85.85%, respectively). Conclusion North-African PGDSs have a low-level of KoM. This emphasizes the need for further efforts to enhance awareness and promote better KoM in this population.
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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 | 0.001 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".