The anesthesia workforce in Canada: a methodology to identify physician anesthesia providers using health administrative data
Bibliographic record
Abstract
BACKGROUND: Safe and timely anesthesia services are an integral component of modern health care systems. There are, however, increasing concerns about the availability of anesthesia services in Canada. Thus, a comprehensive approach to assess the capacity of the anesthesia workforce to provide service is a critical need. Data regarding the anesthesia services provided by specialists and family physicians are available through the Canadian Institute for Health Information (CIHI) but collating the data across delivery jurisdictions has proven challenging. As a result, information related to the activity of physician anesthesia providers is routinely excluded from annual physician workforce reports. Our goal was to develop a novel approach to identifying and characterizing the anesthesia workforce on a pan-Canadian scale. METHODS: The study was approved by the University of Ottawa Office of Research Ethics and Integrity. We developed a methodology to identify physicians who provided anesthesia services in Canada between 1996 and 2018 using data elements from the CIHI National Physician Database. We iteratively consulted with expert advisors and compared the results with Scott's Medical Database, the Canadian Medical Association (CMA) Masterfile, and the College of Family Physicians of Canada membership database. RESULTS: The methodology identified providers of anesthesia services using data elements from the CIHI National Physician Database, including categories of the National Grouping System, specialty designations, activity levels and participation thresholds. Physicians who provided anesthesia services only sporadically and medical residents-in-training were excluded. This methodology produced estimates of anesthesia providers that aligned with other sources. The process we followed was sequential, transparent, and intuitive, and was strengthened by collaboration and iterative consultation with experts and stakeholders. CONCLUSIONS: Using physician activity patterns, this novel methodology allows stakeholders to identify which physician provide anesthesia services in Canada. It is an essential step in developing a pan-Canadian anesthesia workforce strategy that can be used to examine patterns and trends related to the workforce and support evidence-informed workforce decision-making. It also establishes a foundation for assessing the effectiveness of a variety of interventions aimed at optimizing physician anesthesia services in Canada.
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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 | 0.032 | 0.082 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.026 | 0.033 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".