Exploring the Use of YouTube as a Pathology Learning Tool and Its Relationship With Pathology Scores Among Medical Students: Cross-Sectional Study
Bibliographic record
Abstract
BACKGROUND: YouTube is considered one of the most popular sources of information among college students. OBJECTIVE: This study aimed to explore the use of YouTube as a pathology learning tool and its relationship with pathology scores among medical students at Jordanian public universities. METHODS: This cross-sectional, questionnaire-based study included second-year to sixth-year medical students from 6 schools of medicine in Jordan. The questionnaire was distributed among the students using social platforms over a period of 2 months extending from August 2022 to October 2022. The questionnaire included 6 attributes. The first section collected demographic data, and the second section investigated the general use of YouTube and recorded material. The remaining 4 sections targeted the participants who used YouTube to learn pathology including using YouTube for pathology-related content. RESULTS: As of October 2022, 699 students were enrolled in the study. More than 60% (422/699, 60.4%) of the participants were women, and approximately 50% (354/699, 50.6%) were second-year students. The results showed that 96.5% (675/699) of medical students in Jordan were using YouTube in general and 89.1% (623/699) were using it as a source of general information. YouTube use was associated with good and very good scores among the users. In addition, 82.3% (575/699) of medical students in Jordan used YouTube as a learning tool for pathology in particular. These students achieved high scores, with 428 of 699 (61.2%) students scoring above 70%. Most participants (484/699, 69.2%) reported that lectures on YouTube were more interesting than classic teaching and the lectures could enhance the quality of learning (533/699, 76.3%). Studying via YouTube videos was associated with higher odds (odds ratio [OR] 3.86, 95% CI 1.33-11.18) and lower odds (OR 0.27, 95% CI 0.09-0.8) of achieving higher scores in the central nervous system and peripheral nervous system courses, respectively. Watching pathology lectures on YouTube was related to a better chance of attaining higher scores (OR 1.96, 95% CI 1.08-3.57). Surprisingly, spending more time watching pathology videos on YouTube while studying for examinations corresponded with lower performance, with an OR of 0.46 (95% CI 0.26-0.82). CONCLUSIONS: YouTube may play a role in enhancing pathology learning, and aiding in understanding, memorization, recalling information, and obtaining higher scores. Many medical students in Jordan have positive attitudes toward using YouTube as a supplementary pathology learning tool. Based on this, it is recommended that pathology instructors should explore the use of YouTube and other emerging educational tools as potential supplementary learning resources.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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".