Understanding relevance of health research: considerations in the context of research impact assessment
Bibliographic record
Abstract
BACKGROUND: With massive investment in health-related research, above and beyond investments in the management and delivery of healthcare and public health services, there has been increasing focus on the impact of health research to explore and explain the consequences of these investments and inform strategic planning. Relevance is reflected by increased attention to the usability and impact of health research, with research funders increasingly engaging in relevance assessment as an input to decision processes. Yet, it is unclear whether relevance is a synonym for or predictor of impact, a necessary condition or stage in achieving it, or a distinct aim of the research enterprise. The main aim of this paper is to improve our understanding of research relevance, with specific objectives to (1) unpack research relevance from both theoretical and practical perspectives, and (2) outline key considerations for its assessment. APPROACH: Our approach involved the scholarly strategy of review and reflection. We prepared a draft paper based on an exploratory review of literature from various fields, and gained from detailed and insightful analysis and critique at a roundtable discussion with a group of key health research stakeholders. We also solicited review and feedback from a small sample of expert reviewers. CONCLUSIONS: Research relevance seems increasingly important in justifying research investments and guiding strategic research planning. However, consideration of relevance has been largely tacit in the health research community, often depending on unexplained interpretations of value, fit and potential for impact. While research relevance seems a necessary condition for impact - a process or component of efforts to make rigorous research usable - ultimately, relevance stands apart from research impact. Careful and explicit consideration of research relevance is vital to gauge the overall value and impact of a wide range of individual and collective research efforts and investments. To improve understanding, this paper outlines four key considerations, including how research relevance assessments (1) orientate to, capture and compare research versus non-research sources, (2) consider both instrumental versus non-instrumental uses of research, (3) accommodate dynamic temporal-shifting perspectives on research, and (4) align with an intersubjective understanding of relevance.
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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: Methods About the Canadian research system: no · About a Canadian topic: no | Theoretical or conceptual | low |
| gpt | Metaresearch Domain: Evaluation · Genre: Commentary About the Canadian research system: no · About a Canadian topic: no | Theoretical or conceptual | low |
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 | 0.317 | 0.078 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.014 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.004 |
| Insufficient payload (model declined to judge) | 0.000 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".