Ten principles of good interdisciplinary team work
Bibliographic record
Abstract
BACKGROUND: Interdisciplinary team work is increasingly prevalent, supported by policies and practices that bring care closer to the patient and challenge traditional professional boundaries. To date, there has been a great deal of emphasis on the processes of team work, and in some cases, outcomes. METHOD: This study draws on two sources of knowledge to identify the attributes of a good interdisciplinary team; a published systematic review of the literature on interdisciplinary team work, and the perceptions of over 253 staff from 11 community rehabilitation and intermediate care teams in the UK. These data sources were merged using qualitative content analysis to arrive at a framework that identifies characteristics and proposes ten competencies that support effective interdisciplinary team work. RESULTS: Ten characteristics underpinning effective interdisciplinary team work were identified: positive leadership and management attributes; communication strategies and structures; personal rewards, training and development; appropriate resources and procedures; appropriate skill mix; supportive team climate; individual characteristics that support interdisciplinary team work; clarity of vision; quality and outcomes of care; and respecting and understanding roles. CONCLUSIONS: We propose competency statements that an effective interdisciplinary team functioning at a high level should demonstrate.
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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 | 0.029 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.031 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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".