Using Problem-Based Case Studies to Learn About Knowledge Translation Interventions: An Inside Perspective
Bibliographic record
Abstract
Knowledge translation (KT) interventions can facilitate the successful implementation of best practices by engaging and actively involving various stakeholders in the change process. However, for novices, the design of KT interventions can be overwhelming. In this article, we describe our experience as participants in a problem-based case study on planning a KT intervention and reflect on the use of problem-based learning (PBL) to develop knowledge and skills relevant to the KT process. Participants were six fellows and two faculty members attending the 2009 Canadian Institutes of Health Research KT Summer Institute. Participants received a case study asking them to develop a KT intervention with the goal of implementing a stroke response protocol for hospital inpatients. The group was given 5 hours spread over 2 days to complete the learning task. As the members of the small group reflected on their experience with the case study, 4 themes emerged: (1) balancing engaging stakeholders with moving forward; (2) exploring the research gaps and role of the Knowledge-to-Action Framework; (3) investigating methodological approaches for KT research; and (4) experiencing a supportive training environment. Participation in the problem-based case study allowed participants to expand their individual understanding of KT, while fostering the learning experiences of other group members. In a supportive learning environment, participants were able to identify influential stakeholders for the stroke response protocol implementation, discuss potential barriers by stakeholder group, and consider effective KT interventions. Future training initiatives focusing on strengthening KT capacity and knowledge should consider using small-group problem-based case study to facilitate learning.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.060 | 0.068 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.009 | 0.015 |
| Scholarly communication | 0.016 | 0.014 |
| Open science | 0.007 | 0.009 |
| Research integrity | 0.007 | 0.006 |
| 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".