Organizational theory – a three-dimensional tool to analyze and enhance collaboration in healthcare systems
Bibliographic record
Abstract
Purpose Healthcare systems receive criticism from both providers and recipients. The diversity in these systems throughout the world makes innovation and change difficult. However, a structured analysis of healthcare systems is crucial to identify areas for improvement and to share best practices for the betterment of healthcare throughout the world. Design/methodology/approach The paper uses organizational theory as an unbiased tool for evaluating healthcare systems. This theory analyses healthcare systems across five dimensions: environment, culture, social structure, physical structure and technology. This analysis provides an in-depth understanding of the organization's surroundings, formation and function. It offers a lens through which healthcare systems can be envisioned and establishes a vocabulary for communication. Findings Organizational theory presents a multifaceted approach to initiate assessments aiming to enhance existing healthcare systems and customize them to serve all stakeholders within the focused ecosystem. It alters the dynamics of criticism and presents an opportunity to sustainably address unforeseen healthcare challenges in the future. As the author proceeds to understand healthcare organizations through the perspective of organizational theory, the author also uncovers subtle yet crucial issues such as resource dependence, cultural clashes, organizational silence, bureaucracy, hierarchy, ethics, values, engagement and burnout. Originality/value This paper was crafted from a collaborative paper for the final of a master's degree. A collaboration was conceptualized using organisation theory as the tool to align processes and achieve successful outcome. The narrative of the collaboration has been edited and paper presented highlighting the importance of the tool of organisation theory in healthcare systems.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".