Developing a Framework for Understanding Organizational Culture in Healthcare Organizations from a Complex Adaptive Systems Perspective
Bibliographic record
Abstract
Most conceptual models of organizations, including the dominant frameworks for understanding and assessing organizational culture, rely on a mechanistic view of organizational dynamics. Although popular, the mechanistic view is inherently simplistic and fails to address the complexity of contemporary organizations (Boan & Funderburk, 2003; Clancy, 2007; Clancy, 2007; McDaniel & Driebe, 2001; Plsek & Greenhalgh, 2001; Rowe & Hogarth, 2005; Seel, 2000, Seel, 2005). This research project attempted to provide a more comprehensive conceptual tool through developing and assessing a framework based on the key properties of the complex adaptive systems perspective. The new Complex Adaptive Systems – Organizational Culture (CAS-OC) Framework developed in this research was then assessed with relation to use in health care organizations. This framework was developed through a mixed methods research design consisting of three phases. Phase one involved a systematic scoping review of the organizational culture and complex adaptive systems literature. The review of the organizational culture literature resulted in identification and screening of 12,154 peer-reviewed articles and 1,660 grey literature documents. A total of 215 documents met the inclusion criteria and were reviewed resulting in identification of 14 definitions, 26 frameworks and 123 components of organizational culture. The review of the complex adaptive systems literature resulted in confirmation of the five key properties of complex adaptive systems discussed in the literature. Phase two involved a Delphi study conducted with international organizational culture experts. These experts identified 68 components of organizational culture. Based on their conceptual similarity, these components were combined into 17 factors of organizational culture. The results from phases one and two were added to the newly confirmed list of key properties of complex adaptive systems to inform development of the new CAS-OC Framework. Phase three was the assessment phase. The new CAS-OC Framework was presented to professionals in Alberta healthcare organizations for review and assessment as a potential tool. Their assessment confirmed the categories and components already incorporated in the framework and identified 10 additional components they wanted to see included in the framework. These additional components were examined and incorporated into the new framework if they were not already included. Overall, the assessment participants concluded that the framework was feasible, appropriate, meaningful and effective for use in healthcare organizations in Alberta, specifically Primary Care Networks in Alberta. This research contributed to the body of knowledge in four areas. First, this research examined organizational culture using a phased, mixed method approach. Second, this research confirmed the key properties of complex adaptive systems theory discussed in the complexity literature. Third, this research introduced a complex adaptive systems–based definition and framework for understanding organizational culture in Alberta health care organizations to the current body of knowledge. Finally, this research assessed the Feasibility, Appropriateness, Meaningfulness, and Effectiveness of the CAS-OC Framework from a usability perspective. The research concluded with recommendations for future research.
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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 | 0.019 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.010 | 0.006 |
| Science and technology studies | 0.005 | 0.018 |
| Scholarly communication | 0.013 | 0.014 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.004 | 0.005 |
| 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".