The Collaborative Health Care Team: The Role of Individual and Group Expertise
Bibliographic record
Abstract
BACKGROUND: Increasing costs of health care and rapid knowledge growth have led to collaboration among health care professionals to share knowledge and skills. PURPOSES: To characterize the qualitative nature of team interaction and its relation to training health professionals, drawing on theoretical and analytical frameworks from the sociocognitive sciences. METHODS: Activities in a primary care unit were monitored using observational field notes, hospital documents, and audio recordings of interviews and clinical interactions. RESULTS: The demarcation of responsibilities and roles of personnel within the team became fuzzy in practice. Continuous care was provided by primary care providers and specialized care by intermittent consultants. The nature of individual expertise required was a function of the patient problem and the interaction goal. These team characteristics contributed to the reduction of unnecessary and redundant interactions. CONCLUSIONS: Distributed responsibilities allow the team to process massive amounts of patient information, reducing the cognitive load on individuals. The uniqueness of individual professional expertise as it contributes to the accomplishment of team goals is highlighted, suggesting emphasis on conceptual competence in the development of individual professional education programs.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".