AGING: RETHINKING WHAT AND HOW WE TEACH
Bibliographic record
Abstract
Due to changing demographics, it is increasingly necessary for professional nurses to acquire competency in the care of older adults. The specialty of gerontological nursing is caught among health care directives, physician assisted suicide, the position of older adults in society, practice setting challenges (i.e., staffing requirements, workload, blend of staff skill levels), various interests in curricula revisions, and career development. The specialty developed relatively recently in practice and even more recently in education, and as such often lacks the structural and institutional support needed for it to flourish. Described in this poster, through a descriptive case study, is a curriculum re-design initiative undertaken by a University’s Faculty of Nursing (Alberta, Canada). The Faculty restructured its undergraduate nursing curriculum to integrate aging content and related clinical experiences throughout its four year program. It was cognizant of the mandate, through legislation, to educate a generalist nurse at the undergraduate level of nursing education. At the same time, it asked how do we best provide aging content to our undergraduate students? The purpose of this case study is to further understanding of an integrative approach to facilitate learning opportunities for undergraduate nursing students. A description of the work done by the Faculty in moving the integration of aging content from the spark of an idea to reality is provided. The benefits for students are described through feedback from them and from faculty members. Recommendations for continued enhancement of the program, specific to aging content, are included.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.021 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.011 | 0.038 |
| Scholarly communication | 0.017 | 0.021 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.006 | 0.018 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".