Exploration of Organizational and Behavioral Constructs in the Healthcare Context Using AI
Bibliographic record
Abstract
Artificial Intelligence (AI), through both facilitating complex decision-making and through the development of new approaches to work processes, has transformed both academic and healthcare work. In doing so, AI has led us to rethink organizational theory and behavior research. The workplace can be researched in new ways and with new attention to how professionals interact with technology. AI has also led to rethinking our approaches to the operationalization of theoretical constructs, and large language models (LLM) contribute to understanding how to represent concepts through the voice of stakeholders in the workplace. This symposium focuses on developing models of workplace behavior and new ways to study workplaces where technology is transforming behavior as well as by applying AI to how we conceptualize organizational constructs. The organizers of this symposium, Drs. Lee and Banaszak-Holl, explore how AI has transformed analysis of compassion in the clinical workplace. They are joined by Paige Nong, who researches deployment of AI in safety net healthcare organizations; by Abi Sriharan, who will present a case study on AI implementation in a Canadian health system; and by Kejia Hu and Zhenzhen Jia, whose research delves into AI algorithm development and explores implementation actions with AI. Can NLP enable real-world data-driven compassion analysis in palliative care? Author: Seung-Yup Lee; University of Alabama at Birmingham Author: Jane Banaszak-Holl; University of Alabama at Birmingham Author: Kenneth Boockvar; University of Alabama at Birmingham Governing and deploying artificial intelligence in the US healthcare safety-net Author: Paige Nong; University of Minnesota The impact of AI implementation in healthcare: A four-level framework Author: Kejia Hu; University of Oxford Author: zhen jia; Author: Jasper ShiDa Xu; Waseda University Integrating AI-based clinical decision support systems across complex healthcare organizations Author: Abi Sriharan;
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".