The development of a nursing education program evaluation framework for a Bachelor of Nursing program
Bibliographic record
Abstract
Background and Purpose: Nursing education programs must meet education standards set by provincial regulatory and national accrediting bodies (Canadian Association of Schools of Nursing [CASN], 2022; College of Registered Nurses of Manitoba [CRNM], 2018). These standards establish the benchmarks a nursing program must meet to ensure they are providing quality education that meets the current needs of the populations they serve (CASN; CRNM). The purpose of this project was to identify the vital components of a program evaluation framework and to develop a dissemination plan to meet the needs of the local context of an undergraduate nursing program in Western Canada. Methods: To explore undergraduate nursing programs’ approach to program evaluation, I conducted a literature review, consultations with stakeholders, and an environmental scan. Results: A vast amount of literature exists on the importance of program evaluation and preparing for accreditation; however, very little research exists on how to plan, implement, and evaluate program evaluation procedures. Although various stakeholders identified a strategic plan for program evaluation as essential, they also identified many barriers to completing the vast amount of work that program evaluation entails. Conclusion: I developed a draft program evaluation framework and a plan for implementation that will provide a baseline for program evaluation activities. In this report, I describe the development of a framework using Stufflebeam’s (1983) Context Input Process Product (CIPP) evaluation model; explore my development of advanced practice nurse competencies; and outline the dissemination of the evaluation plan which aims to guide a small, rural, undergraduate nursing program through a systematic and sustainable approach to program evaluation.
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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 | 0.170 | 0.091 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.012 | 0.007 |
| Science and technology studies | 0.007 | 0.009 |
| Scholarly communication | 0.011 | 0.011 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.003 | 0.006 |
| 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".