Impact of the COVID‐19 pandemic on the burnout rates of graduating Canadian Urology residents
Bibliographic record
Abstract
OBJECTIVE: To assess the impact of coronavirus disease 2019 (COVID-19) on burnout rates in Canadian Urology trainees. SUBJECTS AND METHODS: A total of 37 chief residents representing all 12 Canadian Urology residency programmes attended a preparatory examination in December 2019 pre-pandemic and 39 chief residents attended virtually in November 2020 during the pandemic. The Maslach Burnout Inventory (MBI) for medical professionals' questionnaire was administered anonymously to both groups. The MBI covers emotional exhaustion, depersonalisation, and personal accomplishment. Descriptive statistics were used to analyse the data. RESULTS: There was a 100% response rate in the convenience sample (n = 37) in 2019 and 64.1% response rate (n = 25) in 2020. Overall, 70% of chief residents in Canadian Urology programmes showed evidence of burnout in 2019 compared to 88% in 2020 (P = 0.101). There was a statistically significant difference between the two cohorts in emotional exhaustion (mean [sd] 16.2 [5.6] in 2019 and 20.2 [6.2] in 2020, P = 0.011) and personal accomplishment scores (mean [sd] 32.2 [4.5] in 2019 and 30.6 [3.6] in 2020, P = 0.039). CONCLUSIONS: This study is the first to examine the impact of the pandemic on burnout rates in Urology trainees. Burnout rates are high in trainees at baseline, and the pandemic appears to have exacerbated emotional exhaustion, and personal accomplishment, but not overall burnout rates. Vigilance and proactive steps need to be implemented to alleviate this crisis.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".