FACTORS INFLUENCING MEDICAL STUDENTS' ATTENDANCE: A CROSS-SECTIONAL STUDY
Bibliographic record
Abstract
Background: Absence of students from the classroom is one of the emerging problems in the medical sciences since recent years. Failure to attend classes disrupts the dynamic teaching-learning environment and causes this environment to become boring and unpleasant. The aim of this study was to evaluate medical students' views on factors affecting their presence in classrooms in Continental Medical CollegeMethods: A cross-sectional study was done on medical students at Continental Medical College, Lahore. A non-probability convenience sampling technique was used. A pre-tested semi-structured questionnaire containing demographic questions, and 13 items on factors affecting student attendance in classrooms on a five-point Likert scale was used to collect data. Data was evaluated using SPSS 25.Results: All 13 questions were categorized in 3 domains: Compulsory, Learning Outcomes, and Motivation. Descriptive statistics showed learning outcome as the major factor influencing student’s attendance followed by compulsory and motivation. Independent sample t test showed no significant difference between both genders. One way ANOVA test showed significant difference in all domains across years of study. Post Hoc Tukey HSD test showed 1st Year students are more likely to view attendance as compulsory and beneficial compared to students in later years.Conclusion: The results indicate that while gender does not play a significant role in students' perceptions of class attendance, the year of study does. First-year students tend to have stronger perceptions of the necessity and benefits of attending classes, which may decrease as they progress through their medical education. This information could be valuable for developing targeted strategies to maintain or improve attendance rates throughout the MBBS program.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| 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 | 0.001 |
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".