Interprofessional Team Training at the Prelicensure Level: A Review of the Literature
Bibliographic record
Abstract
PURPOSE: The authors undertook a descriptive analysis review to gain a better understanding of the various approaches to and outcomes of team training initiatives in prelicensure curricula since 2000. METHOD: In July and August 2014, the authors searched the MEDLINE, PsycINFO, Embase, Business Source Premier, and CINAHL databases to identify evaluative studies of team training programs' effects on the team knowledge, communication, and skills of prelicensure students published from 2000 to August 2014. The authors identified 2,568 articles, with 17 studies meeting the selection criteria for full text review. RESULTS: The most common study designs were single-group, pre/posttest studies (n = 7), followed by randomized controlled or comparison trials (n = 6). The Situation, Background, Assessment, Recommendation communication tool (n = 5); crisis resource management principles (n = 6); and high-fidelity simulation (n = 4) were the most common curriculum bases used. Over half of the studies (n = 9) performed training with students from more than one health professions program. All but three used team performance assessments, with most (n = 8) using observed behavior checklists created for that specific study. The majority of studies (n = 16) found improvements in team knowledge, communication, and skills. CONCLUSIONS: Team training appears effective in improving team knowledge, communication, and skills in prelicensure learners. Continued exploration of the best method of team training is necessary to determine the most effective way to move forward in prelicensure interprofessional team education.
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 | 0.008 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.018 | 0.017 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".