Surgical Team Effectiveness Model: Team Awareness mediates subculture teamwork and communications
Bibliographic record
Abstract
The Institute of Medicine (Kohn et al, 2000) reports that effective team function is a core principle of safe health care systems. Lemieux-Charles and McGuire (2006)’s Integrated Team Effectiveness Model (ITEM) provides a comprehensive and adaptable framework for describing healthcare team effectiveness. Here we adapt the ITEM within the microenvironment of surgical team design based on the ad hoc nature of a Standard Roles Variable Personnel construct (Andreatta, 2010). With the changing nature of personnel, we propose that team monitoring requires a degree of constant awareness to changes in membership and task prioritization throughout the course of a surgery. We propose the concept of Team Awareness, the negotiation of Standard Roles Variable Personnel at the level of the individual as a mediator between teamwork and organizational outcomes. The implications of Team Awareness are necessary for understanding how individuals balance between competing priorities in team subcultures. Describing a group-level context for surgery offers the opportunity to shift agency towards individuals' role in effectiveness. The result is a Surgical Team Effectiveness Model, an understanding of the relationships between teamwork and organizational outcomes in surgery. This model may be valuable for soliciting organizational and team member support for improving teamwork in the operating room.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 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".