Evidence for Health II: Overcoming barriers to using evidence in policy and practice
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
Even the highest quality evidence will have little impact unless it is incorporated into decision-making for health. It is therefore critical to overcome the many barriers to using evidence in decision-making, including (1) missing the window of opportunity, (2) knowledge gaps and uncertainty, (3) controversy, irrelevant and conflicting evidence, as well as (4) vested interests and conflicts of interest. While this is certainly not a comprehensive list, it covers a number of main themes discussed in the knowledge translation literature on this topic, and better understanding these barriers can help readers of the evidence to be more savvy knowledge users and help researchers overcome challenges to getting their evidence into practice. Thus, the first step in being able to use research evidence for improving population health is ensuring that the evidence is available at the right time and in the right format and language so that knowledge users can take the evidence into consideration alongside a multitude of other factors that also influence decision-making. The sheer volume of scientific publications makes it difficult to find the evidence that can actually help inform decisions for health. Policymakers, especially in low- and middle-income countries, require context-specific evidence to ensure local relevance. Knowledge synthesis and dissemination of policy-relevant local evidence is important, but it is still not enough. There are times when the interpretation of the evidence leads to various controversies and disagreements, which act as barriers to the uptake of evidence. Research evidence can also be influenced and misused for various aims and agendas. It is therefore important to ensure that any new evidence comes from reliable sources and is interpreted in light of the overall body of scientific literature. It is not enough to simply produce evidence, nor even to synthesize and package evidence into a more user-friendly format. Particularly at the policy level, political savvy is also needed to ensure that vested interests do not undermine decisions that can impact the health of individuals and populations.
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 | 0.126 | 0.415 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.007 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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 it