Analysis of Employment Data for Interventional Pulmonary Fellowship Graduates
Bibliographic record
Abstract
RATIONALE: Interventional pulmonology (IP) is a maturing field in the subspecialty of pulmonary medicine. Over the last few years, there has been an increased number of listed IP fellowship training programs in the United States and Canada, causing debate about the employment market for IP fellowship graduates. OBJECTIVES: To analyze employment data of IP fellowship graduates. METHODS: Interventional pulmonary fellows, during their IP in-service examination, were surveyed on employment position after graduation. The survey occurred in May or June in the years 2012, 2013, and 2014. An IP position was defined as a position encompassing more than 60% of effort directly toward IP. Geographic location and practice structure (i.e., academic, private/hybrid, and existing or initiating IP practice) were collected and analyzed. MEASUREMENTS AND MAIN RESULTS: There was an 88.5% response rate, with 53 IP fellows participating in the survey. The majority of IP fellowship graduates (75%; 39/52) had positions in academic IP practices. All seven IP private practice positions were to create an IP program. One IP graduate was in a non-IP academic position, four were in non-IP private practice, one was in a research position, and one had no known employment. Most IP fellowship graduates were men (77.4%). Most IP positions were filled in states east of the Mississippi River; only 8 of 53 (15.1%) positions were filled in states west of the Mississippi river. CONCLUSIONS: Despite speculation about the scarcity of academic jobs after fellowship, recently trained IP fellows are more likely to practice in academic settings and join established practices.
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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 | 0.005 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".