Improving a Culture of Knowledge Transfer in a School of Nursing
Bibliographic record
Abstract
Introduction: A series of 19 unfunded knowledge transfer hands-on workshops were implemented (2017–2019) and delivered by 22 facilitators from disciplines of nursing, business, communication, plastic arts, engineering, and community studies. The purpose of this paper is to report on the post-appraisal of the workshops’ implementation; uncovering the attendees’ new ideas and reflections on the content; and the process of expanding knowledge for practice. Methods: The qualitative program evaluation approach, using the standards of utility, feasibility, accuracy, and propriety of a given program, inspired the design of the immediate appraisal of the workshops delivered within a Canadian school of nursing located in a major urban center. Workshop participants (n = 267) included undergraduate and graduate nursing students, contract instructors, and nurses holding administrative positions. Results: Workshops with high attendance included: (a) Structuring Effective Teaching-Learning Encounters in Healthcare Education and Practice; (b) Cancer Pain; (c) Fetal Health Surveillance; and (d) Nurses as Educators in the Clinical Setting. Concerns were raised by the attendees’ low attendance to the following workshops: (a) Mindfulness for Students; (b) Horizontal Violence; and (d) Self-Care for Nursing Students: Alleviating Anxiety. Workshops offered opportunities for attendees to reflect on content and process as related to their future incorporation of learned knowledge in their own education and practice. Conclusions: High engagement in hands-on exercises, spontaneous construction of context, and relaxed moments shared by the attendees indicate a promising culture of sharing and receiving knowledge. A culture of collective, pleasurable learning among attendees was effective in mobilizing powerful forms of nursing knowledge.
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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 | 0.033 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.013 | 0.015 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.003 | 0.019 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".