ASSESSING SLEEP QUALITY AMONG MEDICAL AND DENTAL STUDENTS IN KHYBER PAKHTUNKHWA PROVINCE OF PAKISTAN: A CROSS-SECTIONAL SURVEY
Bibliographic record
Abstract
Objectives: To determine the prevalence of poor sleep and factors associated with it among medical and dental students in Khyber Pakhtunkhwa province of Pakistan.Materials and Methods: A population-based analytical cross-sectional study was carried out from May to September 2022, consisting of 385 undergraduate students from 21 medical and dental colleges. A non-probability convenience sampling technique was employed to assess sleep quality with the help of Pittsburgh Sleep Quality Index (PSQI). Factors associated with poor sleep were determined using binary logistic regression.Results: Among the total, 263 (68.3%) students had poor sleep quality on the PSQI scale. Poor sleep was more likely (OR [95%CI]) among females (2.3 [1.3-3.9]), age≥21years (2.5 [1.1-5.8]), pre-clinical phase (3.1 [1.4-6.5]), BDS students (2.0 [1.1-3.5]), disrupted circadian rhythm (4.2 [2.0-8.6]), and hostelites (1.7 [1.0-2.9]) at signifi cance of P<0.05. Participants’ per day sleep was 5.9hrs (SD=1.2hrs). Regarding PSQI components, the worst performance was noticed in sleep duration (M=1.60, SD=0.95). Of the total, 47 (12.2%) were taking sleep medications, 377 (97.9%) had bedtime phone usage and 242 (62.8%) had no familiarity with “Sleep hygiene”.Conclusion: Findings revealed that poor sleep was prevalent in more than half of the participants. Average sleep duration was suggestive of sleep deprivation indicating for immediate interventions to prevent its potential consequences.
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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 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".