Medical silos, social identity, and duty of care: A call for health leaders to improve transitions of care
Bibliographic record
Abstract
This article explores the concept of medical silos, particularly within hospital systems, and examines their deeper roots in social identity and the fiduciary duty of care of healthcare providers. While traditional perspectives focus on informational and communication barriers, this analysis highlights how professional identity and moral obligations contribute to the persistence of silos. Social identity theory reveals that strong in-group affiliations, formed during medical training and specialization, fosters collaboration within groups but also create divisions between them. Similarly, the fiduciary duty of care, central to ethical medical practice, may inadvertently reinforce silo boundaries in resource-limited environments. By emphasizing the role of centralized leadership, the article proposes that health system managers and leaders, with the broadest possible duty of care, must take action to dismantle these barriers. Recommendations include re-evaluating policies for patient transitions and fostering integrated care pathways to improve overall system flow, rather than simply balancing the agendas of stakeholders within their silos.
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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 | 0.042 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.019 | 0.037 |
| Scholarly communication | 0.019 | 0.026 |
| Open science | 0.003 | 0.029 |
| Research integrity | 0.010 | 0.023 |
| 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".