Bench-to-bedside review: Dealing with increased intensive care unit staff turnover: a leadership challenge
Bibliographic record
Abstract
Critical care leaders frequently must face challenging situations requiring specific leadership and management skills for which they are, not uncommonly, poorly prepared. Such a fictitious scenario was discussed at a Canadian interdisciplinary critical care leadership meeting, whereby increasing intensive care unit (ICU) staff turnover had led to problems with staff recruitment. Participants discussed and proposed solutions to the scenario in a structured format. The results of the discussion are presented. In situations such as this, the ICU leader should first define the core problem, its complexity, its duration and its potential for reversibility. These factors often reside within workload and staff support issues. Some examples of core problems discussed that are frequently associated with poor retention and recruitment are a lack of a positive team culture, a lack of a favorable ICU image, a lack of good working relationships between staff and disciplines, and a lack of specific supportive resources. Several tools or individuals (typically outside the ICU environment) are available to help determine the core problem. Once the core problem is identified, specific solutions can be developed. Such solutions often require originality and flexibility, and must be planned, with specific short-term, medium-term and long-term goals. [...]
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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 | 0.006 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
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".