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Knowledge of misconduct amid North-African post-graduate dental students: A cross sectional study

2025· article· en· W7106146707 on OpenAlexaboutno aff

Bibliographic record

VenueF1000Research · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicAcademic integrity and plagiarism
Canadian institutionsnot available
Fundersnot available
KeywordsMisconductCross-sectional studyPopulationOpen peer reviewAlternative medicineSurvey research

Abstract

fetched live from OpenAlex

<ns3:p> 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 ( <ns3:italic>i.e.</ns3:italic> ; 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. </ns3:p>

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.022
Threshold uncertainty score0.989

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.106
GPT teacher head0.477
Teacher spread0.371 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations0
Published2025
Admission routes1
Has abstractyes

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