The Influence of Gender on the Choice of Radiology as a Specialty Among Medical Students in Saudi Arabia: Cross-Sectional Study
Bibliographic record
Abstract
BACKGROUND: Medical undergraduates are the future doctors of the country. Therefore, determining how medical students choose their areas of specialty is essential to obtain a balanced distribution of physicians among all specialties. Although gender is a significant factor that affects specialty choice, the factors underlying gender differences in radiology are not fully elucidated. OBJECTIVE: This study examined the factors that attracted medical students to and discouraged them from selecting diagnostic radiology and analyzed whether these factors differed between female and male medical students. METHODS: This cross-sectional study conducted at King Abdulaziz University Hospital in Jeddah, Saudi Arabia, used an electronic questionnaire sent to medical students from all medical years during February 2018. Subgroup analyses for gender and radiology interest were performed using the chi-square test and Cramér's V test. RESULTS: In total, 539 students (276 women; 263 men) responded. The most common factor preventing students from choosing radiology as a career was the lack of direct patient contact, which deterred approximately 47% who decided against considering this specialty. Negative perceptions by other physicians (P<.001), lack of acknowledgment by patients (P=.004), and lack of structured radiology rotations (P=.007) dissuaded significantly more male students than female students. Among those interested in radiology, more female students were attracted by job flexibility (P=.01), while more male students were attracted by focused patient interactions with minimal paperwork (P<.001). CONCLUSIONS: No significant difference was found between the genders in terms of considering radiology as a specialty. Misconception plays a central role in students' judgment regarding radiology. Hence, early exposure to radiology, assuming a new teaching method, and using a curriculum that supports the active participation of students in a radiology rotation are needed to overcome this misconception.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.018 | 0.125 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.004 |
| 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 teacher head, 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".