Matters of Change: Nurse Educators’ Experiences Transitioning to a New Curriculum: A Qualitative Approach
Bibliographic record
Abstract
Background: Nurse educators’ transition to a concept-based curriculum requires organizational and administrative support as well as collaborative teamwork. Implementing curricular changes can be challenging. One-on-one interviews were conducted with nurse educators who shared their perspectives on transitioning from a traditional to a newly adopted concept-based baccalaureate nursing curriculum. Objective: This study explores nurse educators’ perceptions of change as they transitioned from a traditional 4-year (eight-semester) baccalaureate nursing curriculum to a 3-year (eight-semester) concept-based curriculum. Method: The researchers used a qualitative descriptive design. Six nurse educators were recruited using purposive sampling. The interviews were conducted by a nurse educator new to the baccalaureate nursing program. Ethical approval was sought and received from the university research ethics board. Interviews were recorded with consent. Data were transcribed verbatim and analyzed using thematic analysis. Findings: Four main themes emerged: personal efficacy: finding confidence and belief in one’s ability to succeed through change; personal challenges: remaining committed while moving away from the familiar, progressing, hoping for positive results, and making adjustments as needed; personal learning: realizing the time needed for personal learning, to translate theory to practice, and to reorganize; and, finally, navigating the experience of change: knowing change is both exciting and daunting.
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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 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.013 | 0.013 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".