Learning Health Systems: A Paradigm Shift in What We Can Do about Digital Health Inequities
Bibliographic record
Abstract
Learning health systems (LHSs) embed social accountability into everyday workflows and can inform how governments build bridges across the digital health divide. They shape partnerships using rapid cycles of data-driven learning to respond to patients' calls to action for equity from digital health. Adopting the LHS approach involves re-distributing power, which is likely to be met with resistance. We use the LHS example of British Columbia's 811 services to highlight how infrastructure was created to provide care and answer questions about access to digital health, outcomes from it and the financial impact passed on to patients. In the concluding section, we offer an accountability framework that facilitates partnerships in making digital health more equitable.
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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 | 0.044 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.013 | 0.116 |
| Scholarly communication | 0.034 | 0.049 |
| Open science | 0.005 | 0.020 |
| Research integrity | 0.015 | 0.025 |
| Insufficient payload (model declined to judge) | 0.007 | 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".