Testing the Collaborative Learning Unit, a New Nursing Practicum Supervision Model
Bibliographic record
Abstract
Introduction: The collaborative learning unit (CLU) is a nursing internship supervision model that has the potential to optimize resources dedicated to clinical education through the sharing of student supervision within a care team. It is a flexible model that can be adjusted to the specific features of the clinical and academic settings looking to adopt it. The testing of this model, however, has not been documented in Quebec. Purpose: This article presents the results of the third cycle of a qualitative active research project designed to support and document CLU adoption by, and for, partners in clinical nursing education. This cycle relates to the experience of the partners during CLU testing in an intensive care setting in Quebec. Methods: The partners’ experience was documented through field observations (33 hours) as well as focus groups (n = 2) and individual interviews (n = 3) with eight partners. The research team performed a theme-based analysis of the data. Results: Nine themes provide an understanding of the partners’ CLU-related experience. They focus on interns’ autonomy, multiple supervisory relationships, and support for learning. Possible improvements are also suggested to address the issues encountered so as to continue work on the model (for example, use of a tool to monitor interns’ progress). Conclusion: The results of this study indicate that mobilizing various partners in a participatory research project enables them to appropriate a new model of internship supervision and results in an overall positive trial experience.
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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 | 0.025 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".