Bibliographic record
Abstract
Learning Health Systems (LHS) are an increasingly common element of health policy reform efforts in a number of jurisdictions. There is little disagreement around the LHS vision, and early adopters provide some development guidance. Despite the attractiveness of the LHS vision, progress on adoption by systems remains slow. In this commentary, we consider one potential reason, namely politics, or the ways in which government bodies, interest groups, and political ideas shape structures and policies. LHS can change the ways that health systems work and interact with payors and populations and thereby create political challenges. The need for upfront new investment to build capacity for LHS activities, the creation of new partnerships or collaborations, increased transparency, and the direct engagement of populations can all create political risks and subsequent barriers. With a broad population health focus that extends across typical political cycles, politics may create an even greater barrier. We suggest that building strong engagement, clear and transparent accountabilities, communities of practice and other vehicles to promote data sharing and transparency, and careful attention to risk management may all help reduce political challenges. Some sets of policies-like value-based care-can support these sorts of changes and accelerate the adoption of LHS.
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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 | 0.021 | 0.040 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.042 |
| Scholarly communication | 0.011 | 0.015 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.017 | 0.030 |
| Insufficient payload (model declined to judge) | 0.003 | 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".