Assessing Educator Burnout in Online Synchronous Teaching in Surgical Disciplines
Bibliographic record
Abstract
BACKGROUND: COVID-19 had a tremendous impact on surgical residency education and training. With little experience or training in using online learning in pedagogically informed ways, some surgical educators and learners experienced the disadvantages of online learning which may have contributed to a greater sense of burnout in the pandemic. The purpose of this study is to survey the level of burnout in surgical educators and assess educators' perspectives on factors that increased or decreased burnout in synchronous online teaching during the pandemic. METHODS: A cross-sectional study consisting of 4 sections was sent to surgical educators at the University of Toronto. Demographic data, validated surveys on burnout and videoconferencing fatigue (the Maslach Burnout Inventory-Educators Survey (MBI-ES) and the Zoom Exhaustion and Fatigue (ZEF) scale respectively), and quantitative questions about teaching factors in synchronous online environments were collected and analyzed. RESULTS: The MBI-ES demonstrated a high degree of emotional exhaustion, and depersonalization and a moderate degree of personal accomplishment in surgeon educators. The ZEF scale noted moderate fatigue across all domains. Although educators noted online learning to be a moderate factor contributing to burnout during the pandemic, there was no correlation between the number of hours or percentage of time teaching online to burnout or zoom fatigue scores. The largest reported contributing factor to online learning leading to burnout was lack of connection to learners, whereas the largest mitigating factor was decreased travel time. INTERPRETATIONS: The study found a moderate degree of exhaustion and burnout among surgical educators in Canada during COVID-19 and examined how aspects of online synchronous learning may have contributed to or helped mitigate these experiences. Based on this, we present approaches and educational theories to improve the online learning experience for surgical educators going forward.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.002 |
| 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".