Navigating and reframing tensions within equity‐centered learning health systems
Bibliographic record
Abstract
Introduction: Canada recently joined a growing list of countries that are establishing national collaboratives to exchange knowledge on and scale learning health systems (LHSs) across geographies and sectors. The first symposium of the pan-Canadian Learning Health Hub was held in June 2024 and included a keynote presentation and breakout discussions on how to operationalize equity in LHSs. Methods: In preparing for the keynote presentation, we examined the literature, reflected on our experiences building LHSs that have an equity focus, and on discussions we have had with other LHS practitioners on where and how equity manifests within a LHS. Results: Through our preparation, we identified three tensions that are inherent to and result from centering equity in LHSs: (i) Divergent definitions and languages of health equity (the tension of language); (ii) rapid learning versus slow engagement (the tension of pace); and (iii) equity as a driver and an outcome (the tension of dual roles). In this analysis, we present how these tensions manifest in the practice of equity and LHSs alongside strategies for navigating and reframing these tensions to catalyze dynamic learning. Conclusion: For individuals and organizations interested in advancing equity-oriented LHSs, in Canada and other jurisdictions, this paper highlights how and why the goal should not be to avoid these tensions, but rather to navigate the push-pull inherent in our contexts with intention and a commitment to transformative action.
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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 | 0.050 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.037 | 0.087 |
| Scholarly communication | 0.031 | 0.017 |
| Open science | 0.005 | 0.032 |
| Research integrity | 0.005 | 0.010 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
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".