Evaluating a learning health system initiative: Lessons learned during <scp>COVID</scp>‐19 in Saskatchewan, Canada
Bibliographic record
Abstract
Introduction: Evaluating a learning health system (LHS) encourages continuous system improvement and collaboration within the healthcare system. Although LHS is a widely accepted concept, there is little knowledge about evaluating an LHS. To explore the outputs and outcomes of an LHS model, we evaluated the COVID-19 Evidence Support Team (CEST) in Saskatchewan, Canada, an initiative to rapidly review scientific evidence about COVID-19 for decision-making. By evaluating this program during its formation, we explored how and to what extent the CEST initiative was used by stakeholders. An additional study aim was to understand how CEST could be applied as a functional LHS and the value of similar knowledge-to-action cycles. Methods: Using a formative evaluation design, we conducted qualitative interviews with key informants (KIs) who were involved with COVID-19 response strategies in Saskatchewan. Transcripts were analyzed using reflexive thematic analysis to identify key themes. A program logic model was created to represent the inputs, activities, outputs, and outcomes of the CEST initiative. Results: Interview data from 11 KIs were collated under three overarching categories: (1) outputs, (2) short-term outcomes, and (3) long-term outcomes from the CEST initiative. Overall, participants found the CEST initiative improved speed and access to reliable information, supported and influenced decision-making and public health strategies, leveraged partnerships, increased confidence and reassurance, and challenged misinformation. Themes relating to the long-term outcomes of the initiative included improving coordination, awareness, and using good judgment and planning to integrate CEST sustainably into the health system. Conclusion: This formative evaluation demonstrated that CEST was a valued program and a promising LHS model for Saskatchewan. The future direction involves addressing program recommendations to implement this model as a functional LHS in Saskatchewan.
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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 | 0.072 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.020 | 0.011 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.005 | 0.012 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".