Methods to stimulate national and sub-national benchmarking through international health system performance comparisons: A Canadian approach
Bibliographic record
Abstract
OBJECTIVE: This paper presents, discusses and evaluates methods used by the Canadian Institute for Health Information to present health system performance international comparisons in ways that facilitate their understanding by the public and health system policy-makers and can stimulate performance benchmarking. METHODS: We used statistical techniques to normalize the results and present them on a standardized scale facilitating understanding of results. We compared results to the OECD average, and to benchmarks. We also applied various data quality rules to ensure the validity of results. In order to evaluate the impact of the public release of these results, we used quantitative and qualitative methods and documented other types of impact. RESULTS: We were able to present results for performance indicators and dimensions at national and sub-national levels; develop performance profiles for each Canadian province; and show pan-Canadian performance patterns for specific performance indicators. The results attracted significant media attention at national level and reactions from various stakeholders. Other impacts such as requests for additional analysis and improvement in data timeliness were observed. CONCLUSIONS: The methods used seemed attractive to various audiences in the Canadian context and achieved the objectives originally defined. These methods could be refined and applied in different contexts.
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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 | 0.139 | 0.171 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.018 | 0.036 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.001 | 0.003 |
| 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".