Measuring the impact of health research
Bibliographic record
Abstract
Measuring the decision-making impact of applied health research should constitute a core function for many research funders and research organizations. Different target audiences warrant different measures of impact. The target audiences for applied health research include the general public, patients (and their families), clinicians, managers (in hospitals, regional health authorities and health plans), research and development officers (in biotechnology firms) and public policy-makers (i.e. elected officials, political staff and civil servants). Making meaningful assessments within peer groups that fund or produce similar types of research knowledge for similar types of target audiences makes more sense than a one-size-fits-all approach to impact assessment. User-pull and interactive measures of impact (i.e. measures of cultural shifts that would facilitate the on-going use of research knowledge to inform decision-making) can supplement more traditional producer-push measures that assess researchers' active efforts to inform decision-making and the outcome of these efforts. Cultural shifts may include the creation of a research-attuned culture among decision-makers and a decision-relevant culture among researchers. Moving beyond whether research was used to examine how it was used is also important. Research knowledge may be used in instrumental, conceptual or symbolic ways. These actions, coupled with on-going refinements to the proposed assessment tool as research evidence evolves, would take us a long way towards assessment and accountability in the health sector.
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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 | 0.281 | 0.559 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.021 | 0.014 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.016 | 0.017 |
| Open science | 0.003 | 0.015 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".