An Innovative Online Knowledge Translation Curriculum in Graduate Education
Bibliographic record
Abstract
BACKGROUND: There is increased acknowledgment of the importance of knowledge translation (KT) in the role of graduate-prepared healthcare practitioners, such as nurses, as change agents in the mobilization of evidence-based knowledge. The offering of flexible educational programming online and hybrid course delivery in higher education is a response to insufficient didactic methods for providing graduate students with the competencies to facilitate KT. AIMS: To describe the development, implementation, and evaluation of a cohort-based, online, innovative KT curriculum using a theoretical approach to KT called the Knowledge-As-Action Framework, which focuses on the knower, knowledge, and context as being inseparable. This process strategically engages with stakeholders to link practice concerns with existing realities, thus providing the best available knowledge to inform KT action in complex healthcare contexts. METHODS: The Model of Evidence-Informed, Context-Relevant, Unified Curriculum Development in Nursing Education guided the cohort-based online KT course process. The development, implementation, and evaluation involved (a) an environmental scan, (b) a literature review, (c) faculty development, (d) curriculum design of two 10-week courses, and (e) a summation of the concurrent participatory evaluation of the two courses, including faculty and student responses. The Knowledge-As-Action Framework is comprised of six interrelated dimensions as part of a "kite" metaphor, with the underlying premise that if any one of the dimensions results in an imbalance, the KT process may be grounded. RESULTS: Evaluation revealed (a) intentionality of the core processes of curriculum work; (b) effectiveness of indicators for evaluating the KT courses; (c) leadership should be added as a learning domain for KT; (d) the Knowledge-As-Action Framework provided an integrated, philosophical, and evidence-based approach to KT; (e) cohort model facilitated a community of inquiry; and (f) the formalized structured approach of the courses with ongoing supervision and mentoring allowed for timely completion. LINKING EVIDENCE TO ACTION: Teaching and learning in an online cohort model created a community of inquiry and facilitated experiential learning. The active engagement of students with their practice-based stakeholders promoted change in clinical settings and enhanced students' professional development to lead change.
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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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".