Exploring Factors Influencing Medical Trainees’ Specialty Choice: Insights from a Nationwide Cross-Sectional Survey in Jordan
Bibliographic record
Abstract
Phenomenon: Choosing a medical specialty is a critical decision that significantly impacts medical students’ future career. Understanding the factors influencing this decision-making process is important for medical educators, policymakers, and healthcare providers to develop effective strategies that support and guide students in making informed decisions. Approach: We distributed an online self-administered questionnaire to clinical-year medical students (Years 4 to 6) and interns from all medical faculties in Jordan. The questionnaire gathered demographic information, specialty preferences, and factors influencing specialty decision-making. We analyzed the data using descriptive statistics and logistic regression. Findings: 1805 participants completed the questionnaire (51.7% women). General surgery was the most preferred specialty among both genders, followed by internal medicine. Women significantly preferred family medicine, pediatrics, obstetrics and gynecology, and dermatology, whereas men significantly preferred urology, orthopedic surgery, neurosurgery, general surgery, and internal medicine. The factors that most strongly influenced respondents’ specialty preferences were the specialty’s perceived stress levels and working hours, whereas the least influential factors were the specialty’s perceived prestige and role models in the specialty. Women’s specialty preferences were significantly more influenced by their family than men’s. Men were substantially more influenced by specialties’ perceived action-orientation and stress levels than women. Insights: Gender significantly influences medical trainees’ specialty preferences in Jordan. Women tended to prefer specialties that provided greater work-life balance, such as family medicine, pediatrics, obstetrics and gynecology, and dermatology, while men were more drawn toward competitive and profitable surgical specialties like orthopedic surgery, neurosurgery, urology, and general surgery. Additionally, family had a stronger influence on women’s decisions, likely due to cultural and social expectations prioritizing marriage and family for women. Career counseling and mentorship programs are needed to provide guidance, support, and networking opportunities that can help women overcome barriers and biases that may hinder their career advancement.
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 | 0.010 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 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".