Engaging Professionals in Sustainable Workplace Innovation: Medical Doctors and Institutional Work
Bibliographic record
Abstract
© 2018 The Authors. British Journal of Management published by John Wiley & Sons Ltd on behalf of British Academy of Management This paper investigates the role of medical professionals in the success and longevity of the implementation of workplace innovation and organizational change in the Accident and Emergency (A&E) Departments of two large public hospitals, in Australia and Canada, during the introduction of process improvement using Lean Management (LM) methodologies. We ask why and how doctors resist, influence or enable LM initiatives in healthcare. Using a qualitative methodology, we contribute to institutional work theory by unpacking the complex forms of boundary and practice work undertaken by key actors who effectively use their professional status and power to enable practice changes to be embedded. Our findings lend support to the importance of the involvement and ownership of senior doctors in the design, introduction and implementation of successful workplace innovation and organizational change. Senior doctors use their professional expertise, positional and political power at the industry, organization and workplace levels to influence strategically the use of resources designated for workplace innovation to improve efficiencies, quality of patient care and maintain their dominance. The significant organizational change achieved reflected the ownership and leadership of the workplace innovation by senior doctors in ‘hybrid roles’ who captured the rhetoric and minimized adversarialism among key stakeholders.
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 | 0.028 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.016 | 0.031 |
| Scholarly communication | 0.014 | 0.007 |
| Open science | 0.001 | 0.018 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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".