The Anatomy of Health Care Team Training and the State of Practice: A Critical Review
Bibliographic record
Abstract
PURPOSE: As the U.S. health care system enters a new era, the importance of team-based care approaches grows. How is the health care community ensuring that providers and administrators are equipped with the knowledge, skills, and attitudes (KSAs) foundational for effective teamwork? Are these KSAs transferring into daily practice? This review summarizes the present state of practice for health care team training described in published literature. Drawing from empirical investigations of training effectiveness, the authors explore training design, implementation, and evaluation to provide insight into the shape, structure, and anatomy of team training in health care. METHOD: A 2009 literature search yielded 40 peer-reviewed articles detailing health care team training evaluations. Guided by 11 focal questions, two trained raters extracted details regarding training design, implementation, evaluation metrics, and outcomes. RESULTS: Findings indicate that team training is being implemented across a wide spectrum of providers and is primarily targeting communication, situational awareness, leadership, and role clarity. Relatively few details indicate how training needs were established. Most studies collected data immediately posttraining; however, less than 30% collected data six months or more posttraining. Content analyses highlight the need for enhanced detail in published training evaluation reports. CONCLUSIONS: In many respects, health care team training implementation and evaluation align with best practices suggested from the science of training, adult learning, and human performance; however, opportunities for improvement exist. The authors suggest several mechanisms for furthering the health care team training evidence base to enhance patient safety and work environment quality for clinicians.
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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 | 0.064 | 0.275 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.027 | 0.022 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.012 | 0.015 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.006 | 0.005 |
| 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".