Bibliographic record
Abstract
Effective healthcare is vital to prevent illnesses and injuries, to provide treatments and rehabilitation from illnesses and injuries, and to enhance physical, psychological, and social health and well-being. Twenty-first-century healthcare has become a “team sport” that requires multidisciplinary teams with diverse knowledge, skills, abilities, perspectives, and wisdom. Multidisciplinary healthcare teams include physicians, nurses, dentists, psychologists, physical and occupational therapists, and other healthcare practitioners; healthcare researchers, scholars, and educators; healthcare administrators and policymakers; as well as patients and patients’ significant others. This volume includes chapters that address multidisciplinary teams from many different professional, scholarly, and experiential perspectives of experts around the globe. The chapters are written by scholars, practitioners, and educators from Canada, Grenada, Iran, Nigeria, Norway, Qatar, South Africa, United Kingdom, and the United States. It is the goal of this volume to increase understanding of what factors improve and detract from effective multidisciplinary teamwork in healthcare in order to improve its application and enhance the well-being of patients, practitioners, and all members of healthcare teams. Topics addressed in this volume include teams and team members, the importance and benefits of teamwork in healthcare, teamwork skills, and enablers, creating and optimizing healthcare teams, team challenges, and educating healthcare professionals for multidisciplinary teams. Each chapter stands alone to make meaningful contributions regarding multidisciplinary teamwork in healthcare. Together, the chapters in this volume provide a valuable and thoughtful discussion of multidisciplinary teams in healthcare along with a comprehensive list of references for readers who want to dig deeper.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.238 | 0.133 |
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; both teacher heads agree on what is shown here.
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".