BUILDING A LEARNING CULTURE THROUGH AN ENHANCED LEARNING PARTNERSHIP IN CONTINUING CARE
Bibliographic record
Abstract
Continuing and long-term aged care environments provide important health and social support services to older adults, but are rarely seen as optimum sites for student learning, or as vibrant spaces for personal and professional growth for staff or residents. Creating a supported learning environment within these care settings is vital to enhance quality of care and quality of life for residents, their families and staff, and to promote effective learning experiences for students. The Faculty of Nursing at the University of Calgary is working with Covenant Care, a non-profit care provider organization, on an innovative partnership aimed at developing a learning culture within a complex care environment in Calgary, Alberta. This enhanced learning partnership is comprised of three core elements: 1) an undergraduate nursing positive placement program (+PPP); 2) research and advanced practice learning opportunities for graduate students; and 3) learning opportunities and workforce development for residents, families and staff. This participatory action research study is exploring and developing a person-centered care and learning culture within a supportive living and hospice setting. This presentation will detail our developing understanding of the core components of a learning culture within long-term care settings, as well as aspects of culture change, including barriers and facilitators, culture change process, and challenges associated with evaluation and sustainability.
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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 | 0.009 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".