Cognitive and Other Strategies to Mitigate the Effects of Fatigue. Lessons from Staff Physicians Working in Intensive Care Units
Bibliographic record
Abstract
RATIONALE: Fatigue is common among physicians and adversely affects their performance. OBJECTIVES: To identify strategies that attending physicians use when fatigued to maintain clinical performance in the intensive care unit (ICU). METHODS: We conducted a qualitative study using focus groups and structured interviews of attending ICU physicians working in academic centers in Canada. MEASUREMENTS AND MAIN RESULTS: In three focus group meetings, we engaged a total of 11 physicians to identify strategies used to prevent and cope with fatigue. In the focus groups, 21 cognitive strategies were identified and classified into 9 categories (minimizing number of tasks, using techniques to improve retention of details, using a structured approach to patient care, asking for help, improving opportunities for focusing, planning ahead, double-checking, adjusting expectations, and modulating alertness). In addition, various lifestyle strategies were mentioned as important in preventing fatigue (e.g., protecting sleep before call, adequate exercise, and limiting alcohol). Telephone interviews were then conducted (n = 15 physicians) with another group of intensivists. Structured questions were asked about the strategies identified in the focus groups that were most useful during ICU activities. In the interviews, the most useful and frequently used strategies were prioritizing tasks that need to be done immediately and postponing tasks that can wait, working systematically, using a structured approach, and avoiding distractions. CONCLUSIONS: ICU physicians reported using a variety of deliberate cognitive and lifestyle strategies to prevent and cope with fatigue. Given the low cost and intuitive nature of the majority of these strategies, further investigations should be done to better characterize their effectiveness in improving performance.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".