Bridging research utilization and knowledge transfer: towards an understanding of knowledge in the workplace
Bibliographic record
Abstract
Despite recent interest, little has occurred to respond to calls for cross-cutting activities in knowledge utilization subfields. The current study draws on research utilization and knowledge transfer to begin bringing subfields together. The current study tests a model integrating knowledge transfer and research utilization factors into the areas of worklife model of burnout. Nurses in Atlantic Canada completed questionnaires assessing their perceptions of organizational culture, attitudes towards research, knowledge transfer behaviours, congruent values, and professional efficacy. Using EQS software, the Hypothesized structural model was analyzed resulting in good model fit. Organizational culture and attitudes towards research predict knowledge transfer behaviours and congruent values that further predict professional efficacy. A multi-group model analysis did not support an interaction between attitudes and organizational culture. The model supports integrating individual and organizational factors of knowledge transfer. The findings have implications for research and interventions focused on improving knowledge transfer and professional efficacy.
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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 | 0.021 | 0.041 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.004 | 0.016 |
| Scholarly communication | 0.013 | 0.021 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.003 | 0.003 |
| 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".