Gathering speed and countering tensions in the rapid learning health system
Bibliographic record
Abstract
The vision of the learning health system (LHS), conceptualized 15 years ago, is for the rapid generation, use, and spread of high-quality evidence that yields better health experiences, outcomes, efficiencies, and equity in everyday practice settings across communities. However, despite the emergence of many useful LHS frameworks and examples to guide adoption, large gaps remain in the speed and consistency with which evidence is generated and used across the range of settings from the bedside to the policy table. Gaps in progress are not surprising, however, given the tensions that predictably arise when key stakeholders-researchers, health systems, and funders-comingle in these efforts. This commentary examines eight core tensions that naturally arise and offers practical actions that stakeholders can take to address these tensions and speed LHS adoption. The urgency for attenuating these tensions and accelerating health system improvements has never been higher. Timeliness, rigor, and prioritization can be aligned across stakeholders, but only if all partners are intentional about the operational and cultural challenges that exist.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.292 | 0.361 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.019 | 0.069 |
| Scholarly communication | 0.033 | 0.057 |
| Open science | 0.007 | 0.041 |
| Research integrity | 0.028 | 0.034 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".