Interprofessional education involving didactic TeamSTEPPS® and interactive healthcare simulation: A systematic review
Bibliographic record
Abstract
The didactic portion of TeamSTEPPS®, which focuses on teaching teamwork and communication, coupled with interactive simulation methods provides a unique interprofessional education (IPE) learning environment. Across the literature there are a wide variety of such programs described, but there is not a consensus on the most effective methodology. A systematic review was therefore undertaken to synthesize, critically appraise, and evaluate existing literature on IPE programs that utilize didactic TeamSTEPPS in conjunction with interactive healthcare simulation. EBSCO and PubMed databases were searched from inception through March 2017 using predetermined inclusion and exclusion criteria. The initial search yielded 66 articles which was reduced to 42 peer-reviewed publications after duplicates were removed. An additional 2 articles were identified via hand search. Therefore, 44 articles were identified and reviewed and 11 studies met all inclusion criteria. Critical appraisal was performed using The Medical Education Research Study Quality Instrument and Newcastle-Ottawa Scale-Education instruments. The outcome measures associated with each program as well as specifics of the didactic portion and interactive healthcare simulation are further explored in this review. It is anticipated that the findings from this systematic review will aid in the development of future evidence-based interprofessional programs.
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 | 0.012 | 0.055 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.013 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".