Gender Inequity in Clinical Clerkships and its Influence on Career Selection: A Cross-Sectional Survey
Bibliographic record
Abstract
Objective: The aim of this study was to identify the frequency, form, and underlying factors contributing to gender inequity experienced by medical undergraduates and assess its influence on their career choices. Method: , 2021-March 30th, 2022. 430 participants were enrolled using a simple-random-sampling-technique. Chi-square/Fisher's Exact tests are employed to assess the relationships between gender and gender-based inequity in various specialties, including their characteristics, influence on career choices, adverse psychological effects, and potential mitigation strategies. Results: Among 430 respondents, 28.6% were male, and 71.4% were female. 89.1% reported gender inequity, evenly distributed in government (80.4%) and private institutions (88.1%). The general surgery and gynecology disciplines stood out, each with a 56% prevalence. In gynecology and surgery clinical-clerkships, both genders experienced similar rates, with females at 54.5% and 42.3%, and males at 56.7% and 61.6%, respectively (P-value = .000*). Disrespect from staff/professors/patients (48.8%) was the most common manifestation, driven by factors like preferences (73.7%), gender superiority (62.6%), societal attitudes (54%), and cultural norms (50.9%). Furthermore, 82.6% of students reported that gender inequity had a negative impact on their career decision (Male = 82.9%;Female = 82.4%, P-value = .899). Additionally, gender inequity also caused demotivation (78.1%), poor self-esteem (67.2%), helplessness/hopelessness (48.6%), and frustration (45.8%). Conclusions: Gender inequity is widely prevalent in the clinical-clerkships, affecting medical students' career decisions and mental health, stressing the need to prioritize and implement solutions at the undergraduate clinical-clerkship level.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".