Interprofessional resuscitation rounds: a teamwork approach to ACLS education
Bibliographic record
Abstract
PURPOSE: We developed and implemented a series of interprofessional resuscitation rounds targeting fourth year nursing and medical students, and junior residents from a variety of specialty programs. METHODS: Each two hour session was conducted in our patient simulation lab, and was held weekly during the academic year. Students were given specific instruction on the roles and responsibilities of resuscitation team members, and then teams of five worked through pre-defined Advanced Cardiac Life Support (ACLS) scenarios on a high fidelity patient simulator. At the end of each session students completed an anonymous evaluation of the program via a standardized questionnaire using Likert rating scales. RESULTS: A total of 222 evaluations (101 nursing students, 42 medical students, and 79 junior residents) were submitted from October 2005 to April 2006. Mean scores reflected a strong consensus that these rounds were valuable for their training, provided a vehicle for understanding team roles in resuscitation, and that these rounds should be mandatory for all medical and nursing trainees. Participants also expressed a desire for additional interprofessional training. CONCLUSION: Despite challenges inherent in teaching a diverse group of learners, these interprofessional resuscitation rounds were rated highly by nursing and medical trainees as valuable learning experiences.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.004 |
| 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".