Using community-based participatory research methods to build the foundation for an equitable integrated health data system within a Canadian urban context
Bibliographic record
Abstract
Abstract Health inequalities amplified by the COVID-19 pandemic have disproportionately affected racialized and equity-deserving communities across Canada. In the Municipality of Peel, existing data, while limited, illustrates that individuals from racialized and equity-deserving communities continue to suffer, receive delayed care, and die prematurely. In response to these troubling statistics, grassroots community advocacy has called on health systems leaders in Peel to work with community and non-profit organizations to address the critical data and infrastructure gaps that hinder addressing the social determinants of health in the region. To support these advocacy efforts, we used a community-based participatory research approach to understand how we might build a data collection ecosystem across sectors, alongside community residents and service providers, to accurately capture the data about the social determinants of health. This approach involved developing a community engagement council, defining the problem with the community, mapping what data is actively collected and what is excluded, and understanding experiences of sociodemographic data collection from community members and service providers. Guided by community voices, our study focused on sociodemographic data collection in the primary care context and identified which service providers use and collect these data, how data are used in their work, the facilitators and barriers to data use and collection. Additionally, we gained insight into how sociodemographic data collection could be respectful, safe, and properly governed from the perspectives of community members. From this study, we identify a set of eight recommendations for sociodemographic data collection and highlight limitations. This foundational community-based work will inform future research in establishing data governance in partnership with diverse and equity-deserving communities.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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 teacher head, 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".