Defining and Assessing Abilities-based Outcomes for Nursing Education: Lessons from the Faculty of Nursing at the University of New Brunswick (Fredericton)
Bibliographic record
Abstract
This session presented the process and results from a history of defining and assessing core abilities for graduates of nursing degree program(s) at the University of New Brunswick, Fredericton. The process has included integrating the Maritimes Provinces Higher Education Commission Degree Level Qualifications Framework (2006) and the competencies for entry-level nurses, as defined by the Nurses Association of New Brunswick (NANB, 2013). Our approach was to use these categorizations of abilities for nursing practice, integrating knowledge, values, skills, attributes and predispositions into broadly defined core domains of ability. In our professional degree program, these domains of practice ability are nested within philosophical commitments to: dialogic relations with students; preparation for nursing practice as a democratic professional; human caring, social justice; and primary health care. These philosophical influences were discussed, noting their relevance in other disciplines. Our session presented specific information about the process of defining the domains of ability that structure the curriculum in our degree programs and the process of defining formative and summative assessment strategies for our learning outcomes. Lessons learned were shared by panel members from serendipitous experiences.
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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 | 0.075 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.007 | 0.010 |
| Scholarly communication | 0.012 | 0.008 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.002 | 0.007 |
| 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".