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 distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".