Communities of Practice in Nursing Academia: A Growing Need to Practice What We Teach
Bibliographic record
Abstract
Although the community of practice (CoP) concept has been heavily utilized in business literature since its inception in the 1990s, it has not been significantly featured in nursing research. With student-centered approaches increasingly infusing nursing classrooms, including opportunities for collaborative learning and the development of student learning communities, it may be time to ask: Do we practice what we teach? Nursing academia faces challenges related to recruitment and retention, scholarly productivity and engagement of new faculty, and increasing demands for collaborative research. Challenges, some would argue, that could be addressed through CoPs; a sentiment reflected in the recent expansion of nursing CoP literature. What is the current state of the application of this concept in nursing academia and what barriers present in the promotion and development of CoPs in the academy? This article addresses these questions and provides guidance for those in search of community.
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 | 0.057 | 0.096 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.018 | 0.040 |
| Scholarly communication | 0.030 | 0.044 |
| Open science | 0.006 | 0.037 |
| Research integrity | 0.024 | 0.026 |
| Insufficient payload (model declined to judge) | 0.010 | 0.004 |
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".