Mapping the landscape: A protocol of a jurisdictional scan of self-identified learning health systems
Bibliographic record
Abstract
Abstract Background There is a growing movement to implement learning health systems (LHS), in which real-time evidence, informatics, patient-provider partnerships and experiences, and organizational culture are aligned to support improvements in care. However, what constitutes a LHS varies based on context and capacity, hindering standardization, scale-up, and knowledge sharing. Further, LHS often use “usual care” as the benchmark for comparing new approaches to care, but disentangling usual care from multifarious care modalities found across settings is challenging. To advance robust LHS, a comprehensive overview of existing LHS including strengths and opportunities for growth is needed. Objectives To scope and identify international existing LHS to: 1) inform the global landscape of LHS, highlight common strengths, and identify opportunities for growth or improvement; and 2) identify common characteristics, emphases, assumptions, or challenges described in establishing counterfactuals in LHS. Methods A jurisdictional scan will be conducted according to modified PRISMA guidelines. LHS will be identified through a search of peer-reviewed and grey literature using Ovid Medline, Ebsco CINAHL, Ovid Embase, Clarivate Web of Science, and PubMed Non-Medline databases and the web along with informal discussions with peer LHS experts. Self-identified LHS will be included if they are described in sufficient detail, either in literature or during informal discussions, according to ≥4 of 10 criteria (core functionalities, analytics, use of evidence, co-design/implementation, evaluation, change management/governance structures, data sharing, knowledge sharing, training/capacity building, equity, sustainability) in an existing framework to characterize LHS. Search results will be screened, extracted, and analyzed to inform two descriptive reviews pertaining to our two main objectives. Data will be extracted according to a pre-specified extraction form and summarized descriptively. Implications This research will characterize the current landscape of worldwide LHS and provide a foundation for promoting knowledge and resource sharing, identifying next steps for the growth, improvement, and evaluation 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 | 0.204 | 0.258 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.007 | 0.009 |
| Bibliometrics | 0.024 | 0.019 |
| Science and technology studies | 0.008 | 0.006 |
| Scholarly communication | 0.008 | 0.011 |
| Open science | 0.009 | 0.011 |
| Research integrity | 0.009 | 0.007 |
| Insufficient payload (model declined to judge) | 0.070 | 0.012 |
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".