Evaluating health research impact: Development and implementation of the Alberta Innovates - Health Solutions impact framework
Bibliographic record
Abstract
Alberta Innovates – Health Solutions (AIHS) is a Canadian-based, publicly funded, not-for-profit, provincial health research and innovation organization mandated to improve health, the health system, and socioeconomic well-being of Albertans through health research and innovation. Investments in health research are substantial and funders face increasing pressure to measure the impact of their investments and demonstrate ‘value for money’. However, measuring impact in this context is a challenge given the lack of agreement on a common approach or gold standard, diverse stakeholder interests, attribution issues, and time lags between investments and the realization of long-term impact. To address these issues and ideally optimize impact, AIHS developed and implemented an impact framework based on a model published by the Canadian Academy of Health Sciences (CAHS). The purpose of this article is to: (1) describe the evolution of the framework’s development and implementation; (2) summarize the results of tests undertaken to verify the suitability, feasibility, and applicability of the CAHS model to the AIHS context; and (3) present the AIHS framework, with discussion focused on the challenges of development and implementation, lessons learned and future plans for its ongoing development and implementation.
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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 | 0.125 | 0.139 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.014 | 0.012 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| 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".