Change management in pharmacy: a simulation game and pharmacy leaders’ rating of 35 barriers to change†
Bibliographic record
Abstract
OBJECTIVES: The primary objective was to rank barriers to change in pharmacy practice. Our secondary objective was to create a simulation game to stimulate reflection and discussion on the topic of change management. METHODS: The game was created by the authors and used during a symposium attended by 43 hospital pharmacy leaders from all regions of Canada (Millcroft Conference, Alton, Ontario, June 2013). The main theme of the conference was 'managing change'. KEY FINDINGS: The simulation game, the rating of 35 barriers to change and the discussion that followed provided an opportunity for hospital pharmacy leaders to reflect on potential barriers to change, and how change might be facilitated through the use of an organized approach to change, such as that described in Kotter's eight-step model. CONCLUSIONS: This simulation game, and the associated rating of barriers to change, provided an opportunity for a group of hospital pharmacy leaders in Canada to reflect on the challenges associated with managing change in the healthcare setting. This simulation game can be modified and used by pharmacy practitioners in other countries to help identify and rank barriers to change in their particular pharmacy practice setting.
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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 | 0.005 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".