Diagnosing and Improving Functioning in Interdisciplinary Health Care Teams
Bibliographic record
Abstract
Interdisciplinary teams play a key role in the delivery of health care. Team functioning can positively or negatively impact the effective and efficient delivery of health care services as well as the personal well-being of group members. Additionally, teams must be able and willing to work together to achieve team goals within a climate that reflects commitment to team goals, accountability, respect, and trust. Not surprisingly, dysfunctional team functioning can limit the success of interdisciplinary health care teams. The first step in improving dysfunctional team function is to conduct an analysis based on criteria necessary for team success, and this article provides meaningful criteria for doing such an analysis. These are the following: a common team goal, the ability and willingness to work together to achieve team goals, decision making, communication, and team member relationships. High-functioning interdisciplinary teams must exhibit features of good team function in all key domains. If a team functions well in some domains and needs to improve in others, targeted strategies are described that can be used to improve team functioning.
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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 | 0.010 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".