A description of a tailored knowledge translation intervention delivered by knowledge brokers within public health departments in Canada
Bibliographic record
Abstract
BACKGROUND: While there is an expectation to demonstrate evidence-informed public health there is an ongoing need for capacity development. The purpose of this paper is to provide a description of a tailored knowledge translation intervention implemented by knowledge brokers (KBs), and reflections on the factors that facilitated or hindered its implementation. METHODS: The 22-month knowledge translation intervention, implemented by two KBs, sought to facilitate evidence-informed public health decision-making. Data on outcomes were collected using a knowledge, skills and behavioural assessment survey. In addition, the KBs maintained reflective journals noting which activities appeared successful or not, as well as factors related to the individual or the organisation that facilitated or hindered evidence-informed decision-making. RESULTS: Tailoring of the knowledge translation intervention to address the needs, preferences and structure of each organisation resulted in three unique interventions being implemented. A consistent finding across organisations was that each site needed to determine where evidence-informed decision-making 'fit' within pre-existing organisational processes. Components of the intervention consistent across the three organisations included one-to-one mentoring of teams through rapid evidence reviews, large group workshops and regular meetings with senior management. Components that varied included the frequency of the KB being physically onsite, the amount of time staff spent with the KB and proportion of time spent one-to-one with a KB versus in workshops. Key facilitating factors for implementation included strong leadership, influential power of champions, supportive infrastructure, committed resources and staff enthusiasm. CONCLUSIONS: The results of this study illustrate the importance of working collaboratively with organisations to tailor knowledge translation interventions to best meet unique needs, preferences, organisational structures and contexts. Organisational factors such as leadership, champions and supportive infrastructure play a key role in determining the impact of the knowledge translation interventions. Future studies should explore how these factors can be fostered and/or developed within organisations. While KBs implemented the knowledge translation intervention in this study, more research is needed to understand the impact of all change agent roles including KBs, as well as how these roles can be maintained in the long-term if proven effective.
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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.009 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 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".