Early effect of the COVID-19 pandemic on the North American cardiothoracic surgery job market
Bibliographic record
Abstract
Background: The present study aims to report the early effect of the coronavirus disease 2019 (COVID-19) pandemic on the cardiothoracic surgery job market in North America. Methods: The Cardiothoracic Surgery Network (CTSNet) job market database was queried, and patterns from January to May for 2019 versus January to May 2020 were compared for trends in job postings and job seekers. Results: Our study is comprised of 395 cardiothoracic surgery job postings, of which 98% were positions located in North America and 63% were academic. The negative impact of the pandemic on the cardiothoracic surgery job market was greatest in the cardiothoracic/cardiovascular combined subspecialty, followed by congenital and adult cardiac surgery, whereas general thoracic surgery experienced an increase in proportion of jobs available. Despite an increase in views per job posted in 2020 vs. 2019 (532 vs. 290), employer views of job seeker curriculum vitae declined over the same time period in 2020 (January, 380 views/month to May, 3 views/month) compared to 2019 (January, 100 views/month to May, 54 views/month). Conclusions: An analysis of job postings from CTSNet suggests a decline in job availability in the North American cardiothoracic surgical job market following declaration of the pandemic with acknowledgement that there is month to month variability and a supply-demand mismatch. The COVID-19 pandemic has had an unprecedented impact on our field, and the ultimate consequences remain unknown.
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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 | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".