Practice and Academic Nurse Educators: Finding Common Ground
Bibliographic record
Abstract
Two university-based schools of nursing and two healthcare regions, supported by a nurses' union, have formed an intersectoral collaboration to develop a practice educator curriculum. The curriculum is designed to increase educator capacity and practice-academic relationships. This article describes the preliminary groundwork among intersectoral partners. Practice and academic educators do not always recognize each others' expertise or share resources effectively. An online survey and focus groups were conducted to identify educators' similar successes and challenges, their perspectives of key criteria necessary to establish practice-academic collaborations and learning environments, and intent to leave. The findings revealed many similarities across sectors, although practice and academic educators had different foci or perspectives that will need to be bridged by the collaboration. Strategies are suggested to maximize educators' commonalities, provide better supports to minimize intent to leave, and ensure sustainability.
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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 | 0.032 | 0.105 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.009 | 0.013 |
| Scholarly communication | 0.011 | 0.010 |
| Open science | 0.002 | 0.020 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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".