Knowledge Translation of Interprofessional Collaborative Patient-Centred Practice: The Working Together Project Experience
Bibliographic record
Abstract
The Working Together (WT) project involved the design and delivery of an online learning resource for healthcare teams in long-term care (LTC) so that knowledge regarding interprofessional collaborative patient-centred practice (ICPCP) could be readily accessed and then transferred to the workplace. The purpose of this paper is to better understand the process of knowledge translation in continuing education for healthcare professionals by documenting our experiences using Lavis et al.’s (2003) organizing framework for knowledge transfer, and highlighting the impact this approach had on the design, development, delivery, and evaluation of the WT program. Fifty-nine pharmacists, physicians, nurses, and nurse practitioners from 17 LTC homes across Ontario, Canada participated in this project. The effectiveness of the knowledge translation of ICPCP through the WT project was evaluated using the Demand-Driven Learning Model (DDLM) evaluation tool (MacDonald, Breithaupt, Stodel, Farres, & Gabriel, 2002) to assess learners’ reactions to the learning resource. Data from quantitative pre-post surveys and qualitative interviews revealed that learners found using the WT online resource to be a satisfactory learning experience, obtained new knowledge and skills regarding ICPCP, transferred knowledge to the workplace, and reported that learning had a positive effect on the residents they cared for.
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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 | 0.036 | 0.060 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.011 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.003 | 0.017 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 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".