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Record W2131588878 · doi:10.1186/1748-5908-9-67

Towards a better understanding of the nomenclature used in information-packaging efforts to support evidence-informed policymaking in low- and middle-income countries

2014· article· en· W2131588878 on OpenAlex

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueImplementation Science · 2014
Typearticle
Languageen
FieldHealth Professions
TopicHealth Policy Implementation Science
Canadian institutionsMcMaster University
FundersWorld Health Organization
KeywordsJargonVariety (cybernetics)Health informaticsHealth services researchHealth administrationHealth policyMedicinePublic relationsSet (abstract data type)Public healthComputer sciencePolitical scienceNursing

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.008
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.089
Threshold uncertainty score0.614

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0010.000
Scholarly communication0.0000.003
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.527
GPT teacher head0.632
Teacher spread0.105 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it