Building sustainable research and data tools and partnerships for a healthy city for all
Bibliographic record
Abstract
Abstract Vancouver is located in a temperate rain forest surrounded by mountains and water. Indigenous communities, including the Nations whose homelands the city occupies, have recognized that the abundance of this place can build healthy communities for many generations. But contemporary Vancouver is not a healthy city for all. Since 2014, Vancouver has adopted a Healthy City Strategy, a master social sustainability plan to address inequities in the social determinants of health at a population scale. This includes a number of population health targets and indicators. However, implementation has been slow, with substantive integration of health into policy not yet achieved. With support from the Partnership for Healthy Cities, we have translated the Healthy City Strategy's indicators into an online dashboard, including disaggregated local data. In addition to public accountability, this tool enables data to be a platform for aligning city policy, action and investment toward common health outcomes. In parallel, we are also piloting more community-generated approaches to health data and indicators, with a focus on co-creating health indicators with Indigenous communities. Preliminary results have shown improvement in collaboration across city departments, leading to increased focus on upstream, preventive work. The dashboard is enabling frameworks for funding and working with the social service sector toward common health goals. Engagement efforts have shown the continued importance of a holistic strategy for health that is co-created with community knowledge and priorities. These interventions must be sustained and integrated into ongoing work. The technical work of developing the dashboard contributes to city-wide efforts toward data monitoring and reporting. Data has shown to be an important tool for beginning conversations, but sharing data about health inequities experienced by communities must be matched with an ongoing commitment to engagement and co-creation. Key messages Population health data can be leveraged through interactive tools to better integrate and align city policies, plans and investments to address the social determinants of health. Engaging and co-creating policy with communities supports upstream, preventive action on health inequities.
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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 | 0.107 | 0.151 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.009 | 0.007 |
| Scholarly communication | 0.029 | 0.023 |
| Open science | 0.006 | 0.040 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.022 | 0.010 |
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".