Integrated knowledge translation to strengthen public policy research: a case study from experimental research on income assistance receipt among people who use drugs
Bibliographic record
Abstract
BACKGROUND: Solutions to complex public health issues should be informed by scientific evidence, yet there are important differences between policy and research processes that make this relationship challenging. Integrated knowledge translation (IKT) is a strategy of sustained stakeholder engagement that intends to address barriers to evidence use. We highlight an example of an IKT project alongside a randomized controlled trial of a public policy intervention that tested different disbursement patterns of income assistance among people who use drugs in Vancouver, British Columbia. METHODS: A case study design was used where an IKT strategy led by a knowledge broker embedded within the research team acts as the case. This case study evaluates the process and effectiveness of the integrated knowledge translation project by measuring intermediate outcomes within a Theory of Change created to map pathways to impact. Content analysis was performed using an evaluation template through document review, post-event evaluations, and detailed tracking of media, knowledge translation activities and requests for information. RESULTS: A host of knowledge translation products synthesized existing research about the harms of synchronized income assistance disbursement and supported stakeholder engagement, facilitating conversation, relationship building and trust with stakeholders. Engagement improved knowledge of the contextual feasibility for system change, and contributed experiential knowledge to study findings. A combination of access to information and stakeholder and media engagement led to increased acknowledgement of the issue by policy makers directly involved in the income assistance system. CONCLUSIONS: This project shows how a multipronged approach to IKT addressed barriers to evidence-informed public policy and successfully contributed to increased public discourse around income assistance policy reform. Additionally, sustained engagement with diverse stakeholders led to improved contextual knowledge and understanding of potential community level impacts that, along with scientific results, improved the evidence available to inform system change. This case study provides insight into the role IKT can play alongside research aimed at public policy improvements. TRIAL REGISTRATION: This IKT project was embedded within the study titled: The impact of Alternative Social Assistance Disbursement on Drug-Related Harm (TASA), known as Cheque Day Study, registered on ClinicalTrials.gov ( NCT02457949 ) May 29, 2015.
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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 | 0.096 | 0.097 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.020 | 0.015 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.005 | 0.013 |
| 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".