Organization Designs for Better Healthcare: Structure and Attentional Processes in Hospitals
Bibliographic record
Abstract
A long tradition of research recognizes that organization design choices plays a key role in orchestrating the information processing of the firm and hence in the outcomes that follow. Reflected in this research is a growing interest in the cognitive consequences associated with variations in organization structure. Yet our understanding of the relationship between organizational structures and its attentional properties is not well established. This is a major shortcoming given that organizations face an ever-increasing need to balance the complexity of competing with often contradictory attentional demands amidst an increasing need for faster learning, greater efficiencies, and better performance. Our symposium addresses both the theoretical and empirical gaps in our understanding of organization design and attention in the context of hospitals, where the attentional demands and the need for better decision making is large. The three empirical papers that will be presented examine the relationship between structure, attentional processes, and organizational behavior in hospitals. They also open up new avenues of research for scholars interested in organization design, attention, and/or healthcare. In addition, a discussion will be led by William Ocasio (Northwestern University), who will utilize the attention-based view of the firm to outline a research agenda for studying the coupling and decoupling of governance and operational channels - both formal and informal - in hospitals, and the consequences for the resulting structural distribution of organizational attention. The Complexity of Environmental and Task Attentional Demands for Hospitals Presenter: William Ocasio; Northwestern U. Information Processing, Attentional Engagement, and Organizational Structure Presenter: John Joseph; U. of California, Irvine Presenter: Alex James Wilson; U. of Minnesota Presenter: Jay (Jinwon) Park; U. of California, Irvine Redesigning an Organization’s Microgeography Presenter: Jillian Chown; Northwestern Kellogg School of Management Presenter: Christopher C. Liu; U. of Oregon Do Individuals Learn Differently from Senior Versus Junior Colleagues’ Failures in Organizations? Presenter: Sunkee Lee; Carnegie Mellon U. - Tepper School of Business Presenter: Jisoo Park; Carnegie Mellon U. - Tepper School of Business
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".