Using a developmental evaluation approach to create a supportive curriculum for first year students
Bibliographic record
Abstract
Background and objectives: First year students experience a significant transition when entering nursing school. The purpose of this research was to explore first year nursing students’ experiences to enhance and innovate the undergraduate nursing program at a large public Canadian university.Methods: The Faculty of Nursing approached their curriculum redesign process utilizing a Developmental Evaluation (DE) framework. Nineteen first year students participated in semi structured interviews and focus groups where they discussed their personal experiences as well as the perceived strengths and weaknesses of the program. After thematic analysis of the data, recommendations were provided to the faculty administration to guide changes made to the new curriculum.Results: Students appreciated opportunities where they could apply their knowledge to real-world situations. Students also expressed many sources of stress, such as inconsistency within and between courses, differing expectations, content, instruction style, and evaluation. They also voiced that there was a lack of communication and support from the Faculty and identified issues with grading systems.Conclusions: The findings from this study highlighted the need to revise the nursing curriculum to provide more student support and foster a positive student-faculty relationship. The current structure of nursing programs has created competition among students, causing a greater focus on obtaining higher grades than on meaningful learning. Integrated learning with authentic experiences was best received by first year students and provided for a collaborative environment. Finally, the findings from this study highlight the opportunities created by utilizing a DE approach to evaluate and innovate nursing curricula.
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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 | 0.060 | 0.083 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".