PREVALENCE AND CORRELATION OF PSYCHOLOGICAL DISTRESS AND COGNITIVE DYSFUNCTION AMONG AI USERS MEDICAL STUDENTS: A CROSS-SECTIONAL STUDY
Bibliographic record
Abstract
ABSTRACT Objective: This study aims to assess the prevalence and explore the correlation between psychological distress and cognitive dysfunction among medical students utilizing artificial intelligence. Methodology: The cross-sectional survey involved 330 medical students from various programs, aged 18 to 30 years, using non-probability convenience sampling. Participants completed the General Health Questionnaire (GHQ-12) to assess psychological distress and the Montreal Cognitive Assessment (MOCA) for cognitive function. Data collection included face-to-face interviews, with informed consent obtained prior to participation. SPSS version 25 was used for statistical analysis. Results: The findings revealed that 54.5% of participants had normal GHQ scores, while 42.8% experienced psychological distress, including 6.7% with severe distress. MOCA results indicated that 73% of participants scored normally, whereas 27% exhibited mild cognitive impairment. GHQ and MOCA scores showed a moderately positive correlation (r = 0.543, p < 0.001), indicating a negative relationship between cognitive performance and psychological distress. Conclusion: The study highlights a significant prevalence of psychological distress and mild cognitive impairment among medical students, emphasizing the need for targeted mental health interventions within medical education to improve overall well-being and academic performance. Key words: Artificial Intelligence, Psychological Distress, Cognitive Function.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".