Building a Data Bridge: Policies, Structures, and Governance Integrating Primary Care Into the Public Health Response to COVID-19
Bibliographic record
Abstract
PURPOSE: The effective integration of primary care into public health responses to the COVID-19 pandemic, particularly through data sharing, has received some attention in the literature. However, the specific policies and structures that facilitate this integration are understudied. This paper describes the experiences of clinicians and administrators in Alberta, Canada as they built a data bridge between primary care and public health to improve the province's community-based response to the pandemic. METHODS: Fifty-seven semistructured qualitative interviews were conducted with a range of primary care and public health stakeholders working inside the Calgary Health Zone. Interpretive description was used to analyze the interviews. RESULTS: SARS-CoV-2 test results produced by the local public laboratory were, initially, only available to central public health clinicians and not independent primary care physicians. This enabled centrally managed contact tracing but meant primary care physicians were unaware of their patients' COVID-19 status and unable to offer in-community follow-up care. Stakeholders from both central public health and independent primary care were able to leverage a policy commitment to the Patient Medical Home (PMH) care model, and a range of existing organizational structures, and governance arrangements to create a data bridge that would span the gap. CONCLUSIONS: Primary care systems looking to draw lessons from the data bridge's construction may consider ways to: leverage care model commitments to integration and adjust or create organization and governance structures which actively draw together primary care and non-primary care stakeholders to work on common projects. Such policies and structures develop trusting relationships, open the possibility for champions to emerge, and create the spaces in which integrative improvisation can take place.
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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 | 0.078 | 0.075 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.022 | 0.041 |
| Scholarly communication | 0.024 | 0.011 |
| Open science | 0.004 | 0.024 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".