Sources of Stress among Medical Students
Bibliographic record
Abstract
Aim: To find out the frequency and relative frequency of stress on medical students of a local medical college, Lahore. Study Design: Cross sectional study Place and Duration of study: Local Medical College with duration one month (Feb 2019) Methods: The study was surveyed on 150 medical students of M.B.B.S part 1 & 11. Questionnaire was divided into three sections including environmental stress, family affairs stress and stress of studies. Results: In the context of environmental stress features in medical students the highest odds of stress in students was problem in time management for study, followed by feeling of bullying and their current feelings of stress, feeling of uncomfortable at the time of dissection and conflict with other student. In the context of stress the highest odds of stress was due to family affairs in medical students was responsibility in regard to family problem, family issues, history of family depression and recently loss of family member. The last context was study related stress include the highest odds of stress in students who not fulfill self-expectations and fail to perform task followed by poor in the studies due disturbances by the class fellows, ineffective copying skills and non-serious attitude toward studies followed by emotional stress and sleeping. Conclusion: It is concluded that good academic environment, as well as problem focused and emotion focused strategies may help to alleviate stress. Keywords: Medical students, Relative frequency of stress.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".