Creating Community: Strengthening Education and Practice Partnerships through Communities of Practice
Bibliographic record
Abstract
Nursing students frequently experience disconnectedness, marginalization and antagonism during their clinical experiences. These experiences limit their ability to fully engage in the social learning that is important to the development of professional skill and identity. Current North American education models emphasize the separation between practice and education, with negative consequences for students and their learning. Re-envisioning the relationship between education and practice using Wenger’s Communities of Practice model promotes the development of mutually beneficial, capacity-building relationships where learning and growth are goals for students and staff alike. Re-creating units as learning organizations committed to learning, reflection, dialogue and quality improvement redefines the education-service relationship and changes the roles of educators and practitioners with respect to the unit learning needs. Wenger’s Communities of Practice model redefines the apprenticeship model of nursing education in ways that allow for more meaningful, effective learning relationships between clinicians, educators and students.
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 | 0.020 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.011 | 0.014 |
| Scholarly communication | 0.010 | 0.016 |
| Open science | 0.003 | 0.033 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".