An exploratory case study investigating the implementation of a novel knowledge translation strategy in a pandemic: the Pandemic Practice Champion
Bibliographic record
Abstract
BACKGROUND: The clinical Pandemic Practice Champion (PPC) role was created in a large tertiary pediatric hospital as a knowledge translation (KT) strategy for implementing COVID-19 evidence-based knowledge. We aimed to describe the core components of the PPC role, the process of implementing the role, and the factors that hindered or facilitated role implementation. METHODS: An exploratory case study was undertaken. Semi-structured interviews were conducted virtually with stakeholders including PPC, managers, and front-line health care professionals (HCP). A directed approach to qualitative content analysis consistent with the Consolidated Framework for Implementation Research (CFIR) guided the analytic process. Inductive analyses and three stages of thematic synthesis were also conducted. RESULTS: Four PPC, 3 managers, and 6 HCP were interviewed. The core components of the PPC role consisted of (a) acting as knowledge experts and educators, (b) problem-solving for complex patient care issues, (c) conducting crisis management, and (d) acting as a resource to management, HCP, and families. Facilitators for successful implementation included access to external information, a supportive organizational context and culture, dedicated time and resources, and leadership support. Lack of clarity of role definition, insufficient time, pandemic uncertainty and fatigue, inability to change infrastructure, and access to external information hindered implementation. CONCLUSION: The PPC role was successfully implemented within a crisis context. Key barriers (role clarity, time, resources) and facilitators (organizational and leadership support) need to be considered when implementing the PPC role in practice. Future studies are needed to determine the intervention effectiveness of the champion role in changing HCP behavior and health outcomes and further examine implementation processes and mechanisms.
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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 | 0.020 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.007 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".