Exploring Arts‐Based Knowledge Translation: Sharing Research Findings Through Performing the Patterns, Rehearsing the Results, Staging the Synthesis
Bibliographic record
Abstract
BACKGROUND: Cultivation of knowledge translation (KT) strategies that actively engage health professionals in critical reflection of their practice and research-based evidence are imperative to address the research-practice gap. While research-based evidence is exponentially growing, our ability to facilitate uptake by nurses and other health professionals has not kept pace. Innovative approaches that extend epistemological bias beyond a singular standpoint of postpositivism, such as the utilization of arts-based methods, expand the possibility to address the complexities of context, engage audience members, promote dissemination within communities of practice, and foster new audiences interested in research findings. AIM: In this paper, we address the importance of adopting a social constructivist epistemological stance to facilitate knowledge translation to diverse audiences, explore various arts-based knowledge translation (ABKT) strategies, and open a dialogue concerning evaluative tenets of ABKT. DISCUSSION: ABKT utilizes various art forms to disseminate research knowledge to diverse audiences and promote evidence-informed practice. ABKT initiatives translate knowledge not based upon a linear model, which views knowledge as an objective entity, but rather operate from the premise that knowledge is socially situated, which demands acknowledging and engaging the learner within their context. Theatre, dance, photography, and poetry are art forms that are commonly used to communicate research findings to diverse audiences. Given the emerging interest and importance of utilizing this KT strategy situated within a social constructivist epistemology, potential challenges and plausible evaluative criteria specific to ABKT are presented. CONCLUSION: ABKT is an emerging KT strategy that is grounded in social constructivist epistemological tenets, and holds potential for meaningfully sharing new research knowledge with diverse audiences. LINKING EVIDENCE TO ACTION: ABKT is an innovative and synergistic approach to traditional dissemination strategies. This creative KT approach is emerging as potent transformational learning tools that are congruent with the relational nature of nursing practice. ABKT facilitates learning about new research findings in an engaging and critical reflective manner that promotes learning within communities of practice.
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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 | 0.057 | 0.103 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.006 | 0.013 |
| Scholarly communication | 0.013 | 0.014 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".