Challenges to the orthopedic resident workforce during the first wave of COVID-19 pandemic: Lessons learnt from a global cross-sectional survey
Bibliographic record
Abstract
BACKGROUND: The COVID-19 pandemic has caused unprecedented concerns on the safety, well-being, quality of life(QOL), and training of the orthopedic resident physician workforce worldwide. Although orthopedic residency programs across the globe have attempted to redefine resident roles, educational priorities, and teaching methods, the global orthopedic residents' perspective with regards to their safety, well-being, QOL, and training, taking into account regional variances remains unknown. METHODS: A 56-item-questionnaire-based cross-sectional survey was conducted online during the COVID-19 pandemic involving 1193 orthopedic residents from 29 countries across six geographical regions to investigate the impact of the COVID-19 pandemic on the well-being, safety, and training of orthopedic residents at a global level, as well as to analyze the challenges confronted by orthopedic residency programs around the world to safeguard and train their resident workforce during this period. RESULTS: < 0.001. Only 46.5% (n = 491) and 58.4% (n = 600) of residents underwent training in critical care or PPE (Personal Protective equipment) usage, respectively; 28.5% (n = 302) residents (majority from Africa, Middle East, South America) reported lack of institutional guidelines to handle infectious outbreaks; 15.4% (n = 160) residents (majority from Africa, Asia, Europe) had concerns regarding availability of PPE and risk of infection. An increase in technology-based virtual teaching modalities was observed. The most significant stressor for residents was the concern for their family's health. Residents' QOL significantly decreased from 80/100 (IQR 70-90) to 65/100 (IQR 50-80) before and during the pandemic, p < 0.001. CONCLUSIONS: The COVID-19 pandemic has significantly impacted the safety, well-being, QOL, and training of the global orthopedic resident physician workforce to different extents across geographical regions. The findings of this study will aid educators, program leaderships, and policy makers globally in formulating flexible, generalizable, and sustainable strategies to ensure resident safety, well-being, and training, while maintaining patient care.
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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 | 0.005 | 0.010 |
| 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 | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".