Prevalence and Purpose of Medical App Usage In Pakistan: A Cross-Sectional Study
Bibliographic record
Abstract
Objective: To determine the prevalence and purpose of medical app usage in Pakistan.Methodology: A cross-sectional study was conducted using a pretested form, distributed via Google Forms. Convenience sampling was used to select a sample of 357 MBBS, BDS, Allied Health, and Nursing students of CMH Lahore Medical College. Data was analyzed using “SPSS version 23.0”. Descriptive statistics were calculated as frequencies. A “p-value” of less than 0.05 was deemed statistically significant.Results: Out of all the participants (357), 40.3% were males, and 59.7% were females, with an average age of20.89 ± 1.61. 95.5% of the participants used intelligent devices, and 86.6% were aware of the medical apps available for use on mobiles. A majority (64.1%) of the students had various medical apps installed on their phones. Almost half of these students (45.1%) were advised by medical educators to use different medical apps for their studies. Most of the students made use of medical apps to search for medical information (49.7%), followed by exam preparations (37.6%), Revision (34.6%), and preparation of presentations (26.4.%). The use of general clinical textbooks and clinical skills guide apps were 22.2% and 23.9%, respectively. 20.5% of the participants did not use Medical Apps for any purpose. A Likert scale showed that students think that Medical Apps are easy to obtain, and many of them frequently use them too. They believe that these apps save time during clinical practice. Medscape was the most common app being used (29.3%), followed by Gray’s Anatomy (25%) and Pharmapedia (23.9%). Conclusion: The common usage of medical apps was recurrent amongst medical students
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".