Politics of change: the discourses that inform organizational change and their capacity to silence
Bibliographic record
Abstract
Changes in healthcare organizations are inevitable and occurring at unprecedented rates. Such changes greatly impact nurses and their work, yet these experiences are rarely explored. Organizational change discourses remain grounded in perspectives that explore and explain systems, often not the people within them. Change processes in healthcare organizations informed by such organizational discourses validate only certain perspectives and forms of knowledge. This fosters exclusionary practices, limiting the capacity of certain individuals or groups of individuals to effectively contribute to change discourses and processes. The reliance on mainstream organizational discourses in healthcare organizations has left little room for the exploration of diverse perspectives on the subject of organizational change, particularly those of nurses. Michel Foucault's work challenges dominant discourse and suggest that strong reliance's on specific discourses effectively disqualify certain forms of knowledge. Foucault's writings on disqualified knowledge and parrhesia (truth telling and frank speech) facilitate the critical exploration of discourses that inform change in healthcare organizations and nurses capacities to contribute to organizational discourses. This paper explores the capacity of nurses to speak their truths within rapidly and continuously changing healthcare organizations when such changes are often driven by discourses not derived from nursing knowledge or experience.
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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 | 0.033 | 0.060 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.020 | 0.113 |
| Scholarly communication | 0.025 | 0.030 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.003 | 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".