Exploring Self-Reported Lifestyle and Career Choices Among Vascular Neurology Fellows (P2.304)
Bibliographic record
Abstract
BACKGROUND: Physician lifestyle and career aspirations affect choice of specialty, choice of practice location and type of practice (academic, private), and future job satisfaction. We describe lifestyle and career choices of Vascular Neurology trainees in the class of 2014. METHODS: Of 80 Vascular Neurology Fellowship directors, 25 (31[percnt]) responded to our request for data, and 21 (26[percnt]) of all Program directors provided us with 28 email addresses of active fellows. Using SurveyMonkey, a questionnaire was administered. A paper questionnaire was also administered to 6 vascular neurology fellows during 2013 International Stroke Conference. RESULTS: Of the 30 responders, stroke fellow age ranged from 26 - 45 years, with (N=9, 30[percnt]) women and (N=21, 70[percnt]) men. Trainees were US/Canada medical graduates (N=14, 30[percnt]), international medical graduates (IMG) not requiring a visa (N=4, 13[percnt]) and IMG requiring a visa (N=12, 40[percnt]). Twelve (40[percnt]) were married without children; 10 (33[percnt]) were single without children. The majority of stroke fellows never smoked (N=23, 77[percnt]), drink occasionally (N= 24, 80[percnt]), consider their eating habits healthy (N= 23, 77[percnt]), and exercises 2-4 times per week (N=16, 53[percnt]). Stroke fellows did not find balance between work and personal life very easy, describing it as moderately easy (N=14, 47[percnt]) and slightly easy (N=9, 30[percnt]). Overall, most (N=18, 60[percnt]) has aspiration to get employment with academic affiliation and (N=12, 40[percnt]) in mix of academic affiliation and private practice. The majority (N=22, 73[percnt]) would choose stroke fellowship again if given the opportunity. CONCLUSIONS: This survey is the first systematic attempt to describe lifestyle and career choices of vascular neurology trainees. Although less than half of the programs responded, none of the queried trainees aspired to a career in private practice, consistent with the academic profile of their training programs.
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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 | 0.003 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".