The Impact of COVID-19 on Plastic Surgery Training in the United Kingdom, Canada and Australia—A Cross-Sectional Study
Bibliographic record
Abstract
Abstract Background Surgical trainees worldwide have been thrust into a period of uncertainty, with respect to the implications COVID-19 pandemic will have on their roles, training, and future career prospects. It is currently unclear how plastic surgery trainees are being affected by COVID-19. This study examined the experience of plastic surgery trainees in Canada, the UK, and Australia to determine trainee roles during the early COVID-19 emergency response and how training changed during this time. Methods A cross-sectional survey-based study was designed for plastic surgery trainees in the UK, Canada and Australia. In total, 110 trainees responded to the survey. Statistical tests were conducted to determine differences in responses, based on year of training and country of residence. Results In total, 9.7% (10/103) of respondents reported being deployed to cover another service. There was a significant difference between redeployment based on country (p = 0.001). Within the UK group, 28.9% of respondents were redeployed. For trainees not deployed, 95.5% (85/89) reported that there has been a reduction in operative volume. Ninety-seven (94.1%) respondents reported that there were ongoing teaching activities offered by their program. The majority of trainees (66.4%) were concerned about their training. There was a significant difference between overall concern and country (p < 0.05). Conclusion In these unprecedented times, training programs in plastic surgery should be aware of the major impact that COVID-19 has had on trainees and will have on their training. The majority of plastic surgery trainees have experienced a reduction in surgical exposure but have maintained some form of regular teaching.
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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 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".