Leveraging Implementation Science in Learning Health Systems
Bibliographic record
Abstract
Many hail using implementation science (IS) as a key activity of high functioning learning health systems (LHSs). Even though IS is considered a LHS accelerant, little guidance exists to support embedding IS studies and applying knowledge generated from IS into everyday LHS functions. In the last decade, Canadian LHSs have emerged under various models. Yet the field remains nascent, with literature often focusing on the implementation and health outcomes of single projects or initiatives. The objective of this dissertation was to generate evidence of how to leverage IS and apply the derived results to support various LHS goals. This dissertation includes four studies. I conducted a social network analysis of IS partnerships in a province-wide learning health system in Alberta, Canada. Qualitative interviews accompanied this study to contextualize the social network analysis results and provide recommendations to strengthen IS collaborations opportunities in LHS settings. I also explored two models for conducting and applying implementation research in LHSs. A comparative case study highlighted how to use the community of practice model to support implementation research in LHS. A longitudinal case study of the Alberta IS Collaborative highlighted how to co-design infrastructure to support the conduct and use of IS in an established LHS. I designed the four studies to generate evidence to support the overarching dissertation objective. The analyses generated important practical insights into how to build foundational implementation research-practice partnerships and infrastructures to leverage IS in LHS settings. Specifically, the studies in this thesis provide evidence for assessing LHS capacity to embed and leverage implementation research-practice partnerships to strengthen LHS activities.
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 | 0.142 | 0.151 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.009 | 0.064 |
| Scholarly communication | 0.028 | 0.025 |
| Open science | 0.005 | 0.025 |
| Research integrity | 0.007 | 0.010 |
| Insufficient payload (model declined to judge) | 0.009 | 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".