Practice Management Knowledge Amongst Plastic Surgery Residents in Canada: A National Survey
Bibliographic record
Abstract
Business and practice management principles are critical components of healthcare provision. Business and practice management is currently undertaught in plastic surgery training programs. The objective was to assess the status of business and practice management teaching amongst plastic surgery programs in Canada. An online survey of all enrolled plastic surgery residents was conducted in 2019 to 2020. Participants were invited to rate their knowledge and confidence about core principles in business and practice management. Sixty-five out of 126 residents responded to this survey (response rate, 51.6%). Only 7.8% of participants had previous business and practice management training; 23.1% reported receiving training in business and practice management during their residency. Participants reported a low level of knowledge and confidence in business and practice management (average Likert score between 3 and 4). Participants reported a high desire for future training in business and practice management particularly in billing and coding (91.2%) and business operations (91.2%). Plastic surgery residents in Canada reported a low level of knowledge and confidence about business and practice management. They desire the inclusion of business and practice management training in future curriculum.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".