An evaluation of the Acute Critical Events Simulation (ACES) course for family medicine residents.
Bibliographic record
Abstract
INTRODUCTION: A 2-year residency must prepare family physicians to provide a broad range of services. In many settings, especially rural and remote practices, family physicians provide emergency and inpatient care and thus encounter critically ill patients. Evidence of the importance of early recognition and aggressive intervention in critical illness is growing. However, opportunities to safely practise critical care skills during residencies are limited. METHODS: The 2-day Acute Critical Events Simulation (ACES) course was offered to all family medicine residents at the University of Ottawa in 2009. The course included lectures, case discussions, hands-on task training and a half-day of high-fidelity simulation. Its aims were to enhance the abilities of residents in family medicine to recognize signs of critical illness, to teach competencies in the early resuscitation and care of such patients, and to increase residents' confidence to include inpatient and emergency care in their practices, or to practise in a rural or remote setting. A postcourse questionnaire, which included Likert-scale and open-ended questions, was distributed to all participants. RESULTS: Thirty-seven participants completed the survey. The ACES course was exceptionally well-received by participants, who reported increases in confidence and perceived competence, as well as intentions to change practice. The course appeared to increase participants' confidence to work in rural or remote areas and include inpatient or emergency medicine services in their practices. CONCLUSION: The ACES course achieved its aims, and participants reported positive outcomes. This highly interactive, simulation-based program may help prepare residents for work in rural or remote communities with critically ill patients.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".