Educating providers of mechanical ventilation: an update
Bibliographic record
Abstract
PURPOSE OF REVIEW: In recent years, research has led to changes in the practice of mechanical ventilation that are associated with improved patient outcome. Unfortunately, many of these recommendations have not been consistently translated to the bedside. Education is an important component of change management, and thus a review of successful education practices, including those that incorporate advances in technology, is timely. RECENT FINDINGS: We are failing to adequately teach important concepts in mechanical ventilation to those healthcare providers in training and to those currently in practice. There are few explicit links between phases of training that ensure achievement of learning objectives related to mechanical ventilation. Targeted multifaceted education initiatives, however, have been shown to reduce the incidence of suboptimal mechanical ventilation care. Advances in simulation technology (table-top simulators, personal computer-based simulators, and high-fidelity patient simulators) have created new educational tools, although it has not been demonstrated how to effectively integrate ventilation simulation into a curriculum map. SUMMARY: A coordinated approach to education about mechanical ventilation should be considered to ensure optimal patient care in a wide variety of clinical settings. Further research is necessary to determine the important characteristics inherent in successful education initiatives, particularly those incorporating new technology such as simulation.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".