Perceived Stress among Students in Medical/Dental and Allied Health Universities in Pakistan due to COVID-19 Pandemic
Bibliographic record
Abstract
Objective: Aim of the study was to explore the perceived stress in students at various medical and dental universities across Pakistan during the COVID 19 pandemic, using a validated scale.Materials and Methods: The study took place at the Institute of Psychiatry (IOP) Rawalpindi Medical University (RMU). Results: About 400 medical students participated countrywide. The final analysis was conducted on 333 participants who completed the survey form. Study participants comprised 69.1% female and 30.9% male students. About 74.5% of the participants represented Punjab province, 1.2% were from Sindh, another 1.2% belonged to Baluchistan, 2.4% were from KPK, and 1.5% were from AJK while 19.2% of them resided in Islamabad. The majority of participants were enrolled in MBBS (78.4%) while the rest were from BDS (3%), Allied Health Sciences (12.9%), Clinical Psychology (3.6%), and Pharm D (2.1%).The mean perceived stress score was 21.34, SD=4.90 suggesting high perceived stress levels. Approximately 4.5% of students perceived low levels of stress, 80.2% perceived moderate stress, whereas 15.3% scored high on the perceived stress scale. Male students had statistically significant (p=0.38) lower stress levels (M=19.99, SD=5.91) as compared to females (M= 21.95, SD= 4.26). Conclusions: Perceived stress level in medical students was alarmingly high and requires urgent intervention by the Medical and Dental Universities for immediate action and policy guidance for early identification and effective management. This can be achieved by delivering targeted e-workshops and evidence-based e-trainings for stress management like psychological first aid and mindfulness techniques.
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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 | 0.003 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".