The Canadian Plastic Surgery Workforce Analysis: Forecasting Future Need
Bibliographic record
Abstract
BACKGROUND: Projecting the demand for plastic surgeons has become increasingly important in a climate of scarce public resource within a single payer health-care system. The goal of this study is to provide a comprehensive workforce update and describe the perceptions of the workforce among Canadian Plastic Surgery residents and surgeons. METHODS: Two questionnaires were developed by a national task force under the Canadian Plastic Surgery Research Collaborative. The surveys were distributed to residents and practicing surgeons, respectively. RESULTS: Two-hundred fifteen (49%) surgeons responded, with a mean age of 51.4 years (standard deviation [SD] = 11.5); 78% were male. Thirty-three percent had been in practice for 25 years or longer. More than half of respondents were practicing in a large urban center. Fifty-nine percent believed their group was going to hire in the next 2 to 3 years; however, only 36% believed their health authority/provincial government had the necessary resources. The mean desired age of retirement was 67 years (SD = 6.4). We predict the surgeons-to-population ratio to be 1.55:100 000 and the graduate-to-retiree ratio to be 2.16:1 within the next 5 to 10 years. Seventy-seven (49%) residents responded. Most were "very satisfied" with their training (61%) and operative experience (90%). Eighty-nine percent of respondents planned to pursue addqitional training after residency, with 70% stating that the current job market was contributing to their decision. Most residents responded that they were concerned with the current job market. CONCLUSIONS: The results of this study predict an adequate number of plastic surgeons will be trained within the next 10 years to suit the population's requirements; however, there is concern that newly trained surgeons will not have access to the necessary resources to meet growing demands. Furthermore, there is an evident shortage of those practicing in rural areas. Many trainees worry about the availability of jobs, despite evidence of active recruitment. The workforce may benefit from structured career mentorship in residency and improved transparency in hiring practices, particularly to attract young surgeons to smaller communities. It may also benefit from a coordinated national approach to recruitment and succession planning.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".