Citizenship status and career self-efficacy: An intersectional study of biomedical trainees in the United States
Bibliographic record
Abstract
Abstract This study examines the intersectional role of citizenship and gender with career self-efficacy amongst 10,803 doctoral and postdoctoral trainees in US universities. These biomedical trainees completed surveys administered by 17 US institutions that participated in the National Institutes of Health Broadening Experiences in Scientific Training (NIH BEST) Programs. Findings indicate that career self-efficacy of non-citizen trainees is significantly lower than that of US citizen trainees. While lower career efficacy was observed in women compared with men, it was even lower for non-citizen female trainees. Results suggest that specific career interests may be related to career self-efficacy. Relative to US citizen trainees, both male and female non-citizen trainees showed higher interest in pursuing a career as an academic research investigator. In comparison with non-citizen female trainees and citizen trainees of all genders, non-citizen male trainees expressed the highest interest in research-intensive (and especially principal investigator) careers. The authors discuss potential causes for these results and offer recommendations for increasing trainee career self-efficacy which can be incorporated into graduate and postdoctoral training.
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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 | 0.002 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 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".