Nurse educators: Introducing a change and evading resistance
Bibliographic record
Abstract
Nurse educators play a pivotal role in strengthening the nursing workforce by designing, implementing, evaluating and revising nursing educational programs. A brief overview from published literature and expert opinions showed that nurse educators are in continuous attempt to introduce changes to the nursing processes for the sake of improvement. This editorial emphasizes the facilitating role of nurse educators in introducing these changes and describes some change management strategies to evade resistance. Resistance is a leading implication of any change that can take the form of either foot-dragging or sabotage. Change management strategies constitute of interdependent processes and variables, therefore it could be a bit complex. Educators may implement an empirical-rational strategy, as nurses are usually willing to accept a change if it is justified and if its benefits are explained. Another approach could be the normative re-educative strategy which is driven by the socio-cultural norms, where educators take into account the impact of change on the work culture (values, attitudes, skills and relationships among staff). The power-coercive strategy is a circumstantial and time efficient approach where educators can utilize the nursing managerial influence to impose the change, but is often associated with a higher chance of resistance. Planning a comprehensive change plan is challenging and educators must be prepared for unanticipated resistance. Nurse educators are required to be innovative, flexible and knowledgeable to select and implement an effective change management strategy.
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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 | 0.020 | 0.086 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.010 | 0.011 |
| Insufficient payload (model declined to judge) | 0.002 | 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".