Aiming for quality: a global compass for national learning systems
Bibliographic record
Abstract
INTRODUCTION: Transforming a health system into a learning one is increasingly recognized as necessary to support the implementation of a national strategic direction on quality with a focus on frontline experience. The approach to a learning system that bridges the gap between practice and policy requires active exploration. METHODS: This scoping review adapted the methodological framework for scoping studies from Arksey and O'Malley. The central research question focused on common themes for learning to improve the quality of health services at all levels of the national health system, from government policy to point-of-care delivery. RESULTS: A total of 3507 records were screened, resulting in 101 articles on strategic learning across the health system: health professional level (19%), health organizational level (15%), subnational/national level (26%), multiple levels (35%), and global level (6%). Thirty-five of these articles focused on learning systems at multiple levels of the health system. A national learning system requires attention at the organizational, subnational, and national levels guided by the needs of patients, families, and the community. The compass of the national learning system is centred on four cross-cutting themes across the health system: alignment of priorities, systemwide collaboration, transparency and accountability, and knowledge sharing of real-world evidence generated at the point of care. CONCLUSION: This paper proposes an approach for building a national learning system to improve the quality of health services. Future research is needed to validate the application of these guiding principles and make improvements based on the findings.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Metaresearch Domain: Evaluation · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Not applicable | low |
| gpt | no category Domain: not available · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Other design | medium |
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 | 0.133 | 0.085 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.008 | 0.037 |
| Scholarly communication | 0.029 | 0.046 |
| Open science | 0.004 | 0.033 |
| Research integrity | 0.011 | 0.016 |
| Insufficient payload (model declined to judge) | 0.006 | 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".