Development of a Simulation-Based Interprofessional Teamwork Assessment Tool
Bibliographic record
Abstract
Background . The Accreditation Council for Graduate Medical Education (ACGME) Milestone projects required each specialty to identify essential skills and develop means of assessment with supporting validity evidence for trainees. Several specialties rate trainees on a milestone subcompetency related to working in interprofessional teams. A tool to assess trainee competence in any role on an interprofessional team in a variety of scenarios would be valuable and suitable for simulation-based assessment.Objective . We developed a tool for simulation settings that assesses interprofessional teamwork in trainees.Methods . In 2015, existing tools that assess teamwork or interprofessionalism using direct observation were systematically reviewed for appropriateness, generalizability, adaptability, ease of use, and resources required. Items from these tools were included in a Delphi method with multidisciplinary pediatrics experts using an iterative process from June 2016 to January 2017 to develop an assessment tool.Results . Thirty-one unique tools were identified. A 2-stage review narrowed this list to 5 tools, and 81 items were extracted. Twenty-two pediatrics experts participated in 4 rounds of Delphi surveys, with response rates ranging from 82% to 100%. Sixteen items reached consensus for inclusion in the final tool. A global 4-point rating scale from novice to proficient was developed.Conclusions . A novel tool to assess interprofessional teamwork for individual trainees in a simulated setting was developed using a systematic review and Delphi methodology. This is the first step to establish the validity evidence necessary to use this tool for competency-based assessment.
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 | 0.064 | 0.145 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.017 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| 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".