The doctor dilemma in interprofessional education and care: how and why will physicians collaborate?
Bibliographic record
Abstract
CONTEXT: Interprofessional educational (IPE) initiatives are seen as a means to engage health care professionals in collaborative patient-centred care. Given the hierarchical nature of many clinical settings, it is important to examine how the aims of formal IPE courses intersect with the socialisation of medical students into roles of responsibility and authority. OBJECTIVES: This article aims to provide an overview of doctor barriers to collaboration and describe aspects of medical education and socialisation that may limit doctor engagement in the goals of interprofessional education. Additionally, the paper examines the nature of team function in the health care system, reviewing different conceptual models to propose a spectrum of collaborative possibilities. Finally, specific suggestions are offered to increase the impact of interprofessional education programmes in medical education. DISCUSSION: An acknowledgement of power differentials between health care providers is necessary in the development of models for shared responsibility between professions. Conceptual models of teamwork and collaboration must articulate the desired nature of interaction between professionals with different degrees of responsibility and authority. Educational programmes in areas such as professionalism and ethics have shown limited success when formal and informal curricula significantly diverge. The socialisation of medical students into the role of a responsible doctor must be balanced with training to share responsibility appropriately. Doctor collaborative capacity may be enhanced by programmes designed to develop particular skills for which there is evidence of improved patient outcomes.
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 | 0.027 | 0.066 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.034 |
| Scholarly communication | 0.009 | 0.013 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.016 | 0.012 |
| 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".