Factors Predicting the Choice of Specialization Among Medical Students and Junior Doctors in Oman: A Cross-Sectional Study
Bibliographic record
Abstract
Background: Choosing a medical specialization is a crucial, career-defining decision for medical students and junior doctors. Objectives: This study aimed to identify variables impacting junior postgraduate doctors' and medical students' choice of specialty in Oman. Methods: A cross-sectional study was conducted at Sultan Qaboos University (SQU) and the Oman Medical Specialty Board (OMSB) in Muscat, Oman. A two-part, self-administered questionnaire was electronically distributed to 247 respondents of different positions and levels of education, including doctors enrolled in the General Foundation Program, interns, and medical students undergoing their junior and senior clinical rotations. Sociodemographic characteristics were compared to determine factors influencing the choice of medical specialization. Results: The most popular choice of specialty was pediatrics (14.6%), followed by family medicine (10.9%), psychiatry (9.3%), and general medicine (8.5%). Medical specialties were chosen more frequently than surgical or diagnostic specialties (60.7% vs. 27.5% and 10.9%, respectively), regardless of gender or current position/level. Significant variations in specialty preferences were observed based on the respondents’ level of paternal educational attainment (P = 0.026) and future desired location of residency (P < 0.001). The factors identified by the participants as most important when selecting preferred specialties were working hours/lifestyle after completion of training (77.3%), positive experiences with a clinician/teacher of a particular specialty (72.5%), and income potential (70.9%). Conclusions: The findings of this study may contribute to healthcare workforce planning strategies aimed at supporting insufficiently staffed specialties, taking into account the needs of patients as well as the interests and preferences of future doctors.
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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 | 0.002 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".