Bibliographic record
Abstract
Change in health care has become rapid and continuous. Much decision-making processes guiding change management are derived from organizational literature which is heavily reflective of managerial perspectives. These perspectives represent and aim to serve only a small subgroup of organizational members. However, change is complex, fragile, and has higher rates of success and sustainability when change management strategies reflect a multitude of organizational voices. There is a dearth of literature exploring the intersect between organizational and nursing discourses on the subject of rapid and continuous change in health care. Multitheoretical, multimethodological, and multidisciplinary informed approaches to methodological decision making are needed to link organizational and nursing discourses in ways that will offer alternative perspectives on the subject of change. Furthermore, critically guided multitheoretical, multimethodological, and multidisciplinary methodologies are timely and important in organizational research. Critically guided research seeks to analyze taken-for-granted assumptions and institutionalized practices seeking alternative perspectives and alternative sources of organizational knowledge. Exploring alternative perspectives from a critical lens recognizes the impact predominant discursive influences have on change management and the subsequent impact on organizational members’ working lives. This article will explore how Kincheloe’s discussions of the critical bricolage serve to support combining critical organizational methodologies (guided by Alvesson and Deetz) with a voice-centered relational method of data analysis (guided by Brown and Gilligan) to create a critical interpretive methodology that explores the voices of nurses as they experience rapid and continuous change in health care.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.020 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.016 | 0.103 |
| Scholarly communication | 0.015 | 0.017 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.007 | 0.013 |
| Insufficient payload (model declined to judge) | 0.005 | 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".