Towards a better understanding of the nomenclature used in information-packaging efforts to support evidence-informed policymaking in low- and middle-income countries
Bibliographic record
Abstract
BACKGROUND: The growing recognition of the importance of concisely communicating research evidence and other policy-relevant information to policymakers has underpinned the development of several information-packaging efforts over the past decade. This has led to a wide variability in the types of documents produced, which is at best confusing and at worst discouraging for those they intend to reach. This paper has two main objectives: to develop a better understanding of the range of documents and document names used by the organizations preparing them; and to assess whether there are any consistencies in the characteristics of sampled documents across the names employed to label (in the title) or describe (in the document or website) them. METHODS: We undertook a documentary analysis of web-published document series that are prepared by a variety of organizations with the primary intention of providing information to health systems policymakers and stakeholders, and addressing questions related to health policy and health systems with a focus on low- and middle-income countries. No time limit was set. RESULTS: In total, 109 individual documents from 24 series produced by 16 different organizations were included. The name 'policy brief/briefing' was the most frequently used (39%) to label or describe a document, and was used in all eight broad content areas that we identified, even though they did not have obviously common traits among them. In terms of document characteristics, most documents (90%) used skimmable formats that are easy to read, with understandable, jargon-free, language (80%). Availability of information on the methods (47%) or the quality of the presented evidence (27%) was less common. One-third (32%) chose the topic based on an explicit process to assess the demand for information from policy makers and even fewer (19%) engaged with policymakers to discuss the content of these documents such as through merit review. CONCLUSIONS: This study highlights the need for organizations embarking on future information-packaging efforts to be more thoughtful when deciding how to name these documents and the need for greater transparency in describing their content, purpose and intended audience.
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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 | 0.278 | 0.335 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.028 | 0.039 |
| Science and technology studies | 0.007 | 0.017 |
| Scholarly communication | 0.034 | 0.026 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".