Utilisation problématique d’Internet et des jeux vidéo chez des étudiants en médecine
Bibliographic record
Abstract
Dependence on the Internet and video games would have an impact on academic performance and mental health.Objective Highlight some of the interest factors in a first-year medical student population who will be subjected during their studies and future to the intensive use of these technologies.Method A self-questionnaire was proposed, from a free access link from February to March 2014, to all first-year medical students at the University of Namur in Belgium. It consisted of questions related to socio-demographic data, Perceived Stress Scale (PSS 14), the Internet Addiction Test (IAT), the Problem Video Game Playing (PVP) and the Montgomery and Asberg Depression Rating Scale (MADRS).Results According to the Internet Addiction Test (IAT), 1% of the students are addicted to the Internet and 24,4% have occasional problematic use. According to Problem Video Game Playing (PVP), 11,4% of the students playing video games are problem gamblers. The data also show significantly higher scores for problematic use of the Internet and video games among stressed students, depressed students and those with poor academic performance.Conclusion We draw attention to the necessary debate between the rational use and the problematic use of new technologies as well as the need for longitudinal prevention from the beginning of studies.
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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 | 0.005 |
| 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 | 0.002 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".