Bibliographic record
Abstract
Worldwide the need for anesthesia services has grown over time. The value for anesthesia services has also been acutely recognized in the wake of a viral pandemic. It is instructive to observe trends in the changing demographics of the anesthesia care workforce and investigate whether its magnitude meets the clinical demand for patients. Simkin et al used health administrative data from the Canadian Institute of Health Information to answer these questions. They reviewed available data from 1996 to 2018. Across this time frame, the anesthesia workforce grew 1.8-fold to 3681 physicians. The average age of the workforce increased by 2.3 years and the annual retirement rate was 3%. The workforce has become more gender balanced but remains predominantly male. Interestingly, family practice physicians who acquired additional training to provide anesthesia services accounted for the majority of care in rural areas of Canada. Their attrition rate was notably high. Although the anesthesia workforce in Canada grew substantially over 22 years, it continues to rely heavily on international medical graduates and family practice physicians. Workforce planning is needed to support the alignment of clinical services with community needs. The reader is strongly encouraged to review the cited article for an in-depth understanding of the concepts summarized in this infographic.
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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.006 | 0.013 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.018 | 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".