How I Work Smarter: A Qualitative Analysis of Emergency Physicians' Strategies for Clinical and Non-clinical Productivity
Bibliographic record
Abstract
Introduction Emergency physicians' (EP) clinical and professional non-clinical environments can be stressful and lead to burnout. However, some EPs thrive in these environments. To date, there is limited research investigating the strategies that successful EPs use to be maximally productive. Methods A snowball sampling technique was used to identify peer-nominated EPs who were, within their community of practice, subjectively felt to be successful and efficient. Participants answered a standardized set of questions addressing their efficiency patterns that were published as part of the "How I Work Smarter" blog series on the Academic Life in Emergency Medicine website. Two reviewers performed an inductive qualitative thematic analysis to code and summarize their responses and develop a thematic framework that described patterns of EP productivity. Results Two themes, communication and efficiency, were applicable in the clinical and non-clinical arenas. Location and environment was a major theme in the non-clinical arena. The themes task management and prioritization, tools for wellness, and motivators spanned both environments. Each theme included several strategies that were felt by the respondents to improve productivity and efficiency. Conclusion We described a thematic framework of productivity strategies for EPs that may increase productivity, improve work-life balance, and decrease burnout. EPs interested in increasing their efficiency both within and beyond the clinical area may consider adopting these strategies.
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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 | 0.026 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.008 | 0.009 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".