Academic careers in global pulmonary and critical care medicine: perspectives from experts in the field
Bibliographic record
Abstract
Academic global pulmonary/critical care medicine (PCCM) remains a relatively novel concept not fully embraced by all training programs, so PCCM early-career professionals may have little guidance in building successful careers in this field. To highlight various approaches used by current PCCM faculty to incorporate global health into their academic careers, speakers from a global health careers mini symposia held at the 2017 and 2018 American Thoracic Society International Conferences were invited to submit perspectives reflecting on academic PCCM and global health. The collection of essays was collated into a single manuscript. Eight current global PCCM faculty from diverse geographic and professional backgrounds provide experiential guidance for early-career professionals interested in global academic PCCM. Trainees and junior faculty interested in academic global PCCM will find innumerable obstacles to developing this non-traditional career pathway, but there exist diverse pathways to success.
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 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 | 0.036 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.019 | 0.011 |
| Scholarly communication | 0.016 | 0.013 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.007 | 0.016 |
| Insufficient payload (model declined to judge) | 0.006 | 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".