Simulation as a tool for developing knowledge mobilisation strategies: Innovative knowledge transfer in youth services
Bibliographic record
Abstract
While there are excellent models of knowledge mobilisation (KMb) that address the opportunity for co-production and sharing of best practice knowledge among human service professionals, it remains unclear whether these models will work in less formal settings like community-based non-government organisations (NGOs) where there are fewer resources for KMb. For three days, 65 policy-makers, senior staff of NGOs, mental health professionals, KMb specialists and youth participated in a set of simulation exercises to problem solve how to mobilise knowledge in less formal settings that provide services to children and youth in challenging contexts (CYCC). Based on simulation exercises used in other settings (such as the deployment of international aid workers), participants were first provided with reports synthesising best practice knowledge relevant to their workplaces. They then engaged in an appreciative inquiry process, and were finally tasked with developing innovative strategies for KMb. Observation notes and exit interviews were used to evaluate the process and assess impact. Findings related to the process of the simulation exercises show the technique of simulation to be useful but that it requires effort to keep participants focused on the task of KMb rather than the content of best practices within a focal population. With regard to developing innovative KMb strategies, findings suggest that service providers in less formal community-based services prefer KMb activities that promote one-to-one relationships, including the participation of youth themselves, who can speak to the effectiveness of the interventions they have experienced. Unexpectedly, the use of electronic communication, including social media, was not viewed very positively by participants. These results suggest that the use of simulation to search for innovative KMb strategies and to problem solve around barriers to KMb has the potential to inform new ways of co-producing and sharing best practice knowledge among human service providers.Keywords: simulation, knowledge mobilisation, high-risk youth, community-based mental health, knowledge brokers, barriers to knowledge exchange
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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 | 0.009 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".