Health workforce reform, interprofessional education and practice and the division of labour
Bibliographic record
Abstract
In recent years governments around the world have been bending their will toward increasing collaborative practice amongst health care professionals. Although interprofessional learning has been on the agenda since the 1950s, to date there has been mixed success in bringing the disparate range of health professionals in the health care system together in a coherent and systematic way. Surprisingly, there has been limited sociological analysis of this phenomenon with no identifiable seminal text that critical analyses the issues facing the development of successful inter-professional practice in health. This edited collection to redress this by providing the conditions for critical engagement with inter-professional issues through developing a critical sociology of interprofessional health care practice. The core strength of the book is the meditations, case studies, evaluations and theoretical reflections on the practice of inter-professional collaboration in health by preeminent scholars from Australia, Canada and the United Kingdom. The book provides a sophisticated critical inquiry that uses a wide array of multi-disciplinary conceptual tools to study the phenomenon of interprofessional practice in a way that is easily understood by both instructors and students in the fields of medicine, allied health and nursing.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".