Level of Perceived Stress and Coping Mechanisms among MBBS Students at Government Medical College: A Prospective Observational Study from Delhi
Bibliographic record
Abstract
strategies that humans use to reduce, tolerate, and master stressful situations.7 These strategies are broadly divided into problem-focused and emotion-focused.The goal of problemfocused strategies is to alter the circumstance by planning, skill development, or problem-solving, whereas emotion-focused strategies, on the contrary, aim to alter the stressful emotion itself IntroductIonStress, according to Lazarus, is caused by an individual's belief that they lack the resources to deal with a perceived circumstance from the past, present, or future. 1 Academic, psychosocial, and health-related stressors are the three most common sources of stress in medical colleges.2 Academic stressors, especially tests/ exams, were identified as the most common source of stress for all students.3 Throughout this time, students should gain sufficient professional knowledge, competency, and attitudes to equip themselves to deal with lifelong professional difficulties on their own.Extensive learning and rigorous training can harm a student's physical and mental health by causing reduced focus, decreased attention, the desire to cheat on tests, sleeping issues, substance misuse, and unacceptable social conduct.4 Chronic stress can reduce productivity and could lead to a serious public health problem.Stress in medical students is much higher when compared to engineering students, as revealed in a study conducted by Waghachavare et al. 5 Another study in Canada by Rahimi et al. 6 found that stress among medical students was higher compared to the general population.To reduce, accept, and manage these stressful situations, humans frequently employ different coping mechanisms.Coping mechanisms are the psychological, emotional, and/or behavioral
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".