Current Approaches To and Implementation of Information Environment Assessments in the Context of Public Health: Rapid Review
Bibliographic record
Abstract
Background: With the advances in digital information sharing channels, democratization of content, and access, as well as social shifts in information exchange, we live in increasingly complex information environments. How people process and manage this is layered with multiple determinants that can impact information seeking, health behaviors, and public health. Understanding the dynamics of the information environment in priority populations and its impact on communities and individuals is critical for those working in public health and health emergencies. Objective: This study aimed to provide an overview of the approaches to and implementation of information environment assessments as they relate to public health and health emergencies. Methods: We conducted a rapid scoping review of the approaches to, and implementation of information environment assessments. The search followed guidance from the Joanna Briggs Institute on conducting systematic scoping reviews, and our reporting is in line with the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines for scoping reviews. We included both academic and gray literature in the English language. As this is an emerging field, an additional step involved input from an informal expert group to identify any further tools or approaches. Studies that assessed, described, or discussed approaches to assessing the information environment were included. We excluded papers where the information environment was not the primary focus, or the focus was on individual components only. Two authors (BKW and SVM) independently screened results for inclusion. Results: A total of 17 publications were identified through the structured literature and internet searches, with an additional 5 sourced from the informal expert group. The review highlighted a significant variety in the breadth and number of domains covered in an assessment, including information needs, seeking, access, production, engagement, information quality, and reach. Some assessments adopted a comprehensive, systems-oriented approach, examining factors influencing information beyond the individual level to encompass broader systemic dynamics, while others were significantly narrower in scope. Conclusions: The COVID-19 pandemic has intensified interest in understanding how the information environment shapes people's access to, engagement with, and ability to act on health information. Assessing the information environment is a critical step in identifying and understanding barriers and facilitators that impact different populations and identifying opportunities for strengthening systems. However, a universally accepted approach for such assessments in public health and health emergencies is currently lacking. This paper contributes to the literature by synthesizing current knowledge on assessment tools and frameworks, providing a foundation for future research and development in this area.
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How this classification was reachedexpand
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 | 0.009 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".