Identifying mechanisms for facilitating knowledge to action strategies targeting the built environment
Bibliographic record
Abstract
BACKGROUND: In recent years, obesity-related diseases have been on the rise globally resulting in major challenges for health systems and society as a whole. Emerging research in population health suggests that interventions targeting the built environment may help reduce the burden of obesity and type 2 diabetes. However, translation of the evidence on the built environment into effective policy and planning changes requires engagement and collaboration between multiple sectors and government agencies for designing neighborhoods that are more conducive to healthy and active living. In this study, we identified knowledge gaps and other barriers to evidence-based decision-making and policy development related to the built environment; as well as the infrastructure, processes, and mechanisms needed to drive policy changes in this area. METHODS: We conducted a qualitative thematic analysis of data collected through consultations with a broad group of stakeholders (N = 42) from Southern Ontario, Canada, within various sectors (public health, urban planning, and transportation) and levels of government (federal, provincial, and municipalities). Relevant themes were classified based on the specific phase of the knowledge-to-action cycle (research, translation, and implementation) in which they were most closely aligned. RESULTS: We identified 5 themes including: 1) the need for policy-informed and actionable research (e.g. health economic analyses and policy evaluations); 2) impactful messaging that targets all relevant sectors to create the political will necessary to drive policy change; 3) common measures and tools to increase capacity for monitoring and surveillance of built environment changes; (4) intersectoral collaboration and alignment within and between levels of government to enable collective actions and provide mechanisms for sharing of resources and expertise, (5) aligning public and private sector priorities to generate public demand and support for community action; and, (6) solution-focused implementation of research that will be tailored to meet the needs of policymakers and planners. Additional research priorities and key policy and planning actions were also noted. CONCLUSION: Our research highlights the necessity of involving stakeholders in identifying inter-sectoral solutions to develop and translate actionable research on the built environment into effective policy and planning initiatives.
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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 | 0.129 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.010 | 0.039 |
| Scholarly communication | 0.017 | 0.018 |
| Open science | 0.006 | 0.023 |
| Research integrity | 0.008 | 0.006 |
| 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".