Evaluating health research impact: Development and implementation of the Alberta Innovates - Health Solutions impact framework
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.357 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it