Keeping the lines open: Exploring communication around nurse education between academia and clinical placement areas
Bibliographic record
Abstract
It is recognized that there are two significant parts to the process of educating nurses: knowledge acquisition and application. Even as much of the knowledge acquisition has formally transitioned to higher education institutions for many nursing programmes, there is a continued need for strong clinical placements to support application of this knowledge into practice. Competing priorities of service and education can make collaboration a challenge, and communication is frequently noted to be a key factor in developing and sustaining effective partnerships. This study was undertaken to explore how communication around nurse education takes place within the diverse partnerships found within nurse education. Semi-structured interviews were done with participants involved in nurse education from both academic and clinical areas. Using a grounded theory approach, each interview was analyzed, compared and contrasted. This allowed four significant categories to emerge, including Foundation (Purpose and Philosophy), Descriptors (Mode and Form), Variables (Concepts of Lack, Time and Relationship) and Outcomes (Frustration, Ambiguity and Engagement). Communication is recognized as necessary for successful holistic nurse education, and all involved and invested in educating nursing students can recognize the part they can have in addressing personal and systemic communication processes.
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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 | 0.065 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.013 | 0.017 |
| Scholarly communication | 0.015 | 0.014 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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".