The historical social positioning of nursing and medicine: implications for career choice, early socialization and interprofessional collaboration
Bibliographic record
Abstract
For almost half a century, research has identified that effective teamwork is essential in order to enhance care provision and health outcomes for patients. Although the value of teamwork is well-recognized in healthcare, the historically rooted dynamics of workplace relationships create a myriad of challenges to creating collaborative teams. Understanding the history of interpersonal dynamics between health professionals can provide direction for future interprofessional education and collaboration strategies. The aim of this paper is to provide a historical overview of the social positioning of nursing and medicine in the context of interprofessional collaboration. Few professions work as closely as nursing and medicine. Despite the well-recognized benefits of interprofessional collaboration, these two professions are often socially positioned in opposition to one another and depicted as adversarial. This analysis will seek to advance our understanding of the historical roots between these two professions and their relationships with and among each other in relation to career choice, early socialization and patient care delivery. An exploration of the historical social positioning of nursing and medicine can provide an enhanced understanding of the barriers to interprofessional collaboration and inform future successes in interprofessional education and practice among all health and social care professions.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.003 | 0.010 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".