Interdisciplinary education and teamwork: a long and winding road
Bibliographic record
Abstract
PURPOSE: This article examines literature on interdisciplinary education and teamwork in health care, to discover the major issues and best practices. METHODS: A literature review of mainly North American articles using search terms such as interdisciplinary, interprofessional, multidisciplinary with medical education. MAIN FINDINGS: Two issues are emerging in health care as clinicians face the complexities of current patient care: the need for specialized health professionals, and the need for these professionals to collaborate. Interdisciplinary health care teams with members from many professions answer the call by working together, collaborating and communicating closely to optimize patient care. Education on how to function within a team is essential if the endeavour is to succeed. Two main categories of issues emerged: those related to the medical education system and those related to the content of the education. CONCLUSIONS: Much of the literature pertained to programme evaluations of academic activities, and did not compare interdisciplinary education with traditional methods. Many questions about when to educate, who to educate and how to educate remain unanswered and open to future research.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".