Learning on the periphery: A pilot study of an undergraduate nursing student communities of practice model
Bibliographic record
Abstract
Objective: Nursing shortages have led to an increased student nurse education and a greater need for work integrated learning among limited health services. A Communities of Practice student placement model was developed to address this deficit, while facilitating greater peer-to-peer learning, and incidental, yet essential, support and learning between junior and senior students. An exploratory study was undertaken to examine the experiences of key stakeholders, students and clinical staff regarding the Communities of Practice model.Methods: After implementation interviews were conducted with six (n = 6) students and three (n = 3) nursing staff, two (n = 2) nurse managers, and one (n = 1) clinical educator. Interviews examined the benefits and challenges of the new model, while further guiding its refinement. Interview data were analysed thematically.Results: The Communities of Practice student placement model, although met with initial hesitancy, was indicated to be a positive learning experience for all participants. Specifically, five key themes emerged, including increased support for junior students, extended learning among senior students, unexpected discoveries for staff and students, workload decision-making and implications for staff, followed by the need for adaptability and further insights to modify the model.Conclusions: The study demonstrated the capacity to increase student placement numbers, while effectively increasing the level of support, mentorship, and learning among students, and assisting nurses in their roles. Overall, the model has also been suggested to offer the near-peer support desperately needed for junior students, while at the same time, offering more senior students the foundation upon which to develop their leadership skills.
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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 | 0.015 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".