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Record W4413850935 · doi:10.2196/72165

Current Approaches To and Implementation of Information Environment Assessments in the Context of Public Health: Rapid Review

2025· article· en· W4413850935 on OpenAlexvenueno aff
Becky K White, Fernan Talamayan, Tara Rose Aynsley, Richard Bahizire Riziki, Catherine Bertrand-Ferrandis, Kai Von Harbou, Rocio Lopez Inigo, Thomas Moran, Reuben Samuel, David Scales, Sandra Varaidzo Machiri

Bibliographic record

VenueJMIR Infodemiology · 2025
Typearticle
Languageen
FieldHealth Professions
TopicHealth Literacy and Information Accessibility
Canadian institutionsnot available
FundersWorld Health Organization
KeywordsPreprintContext (archaeology)Public healthCurrent (fluid)Computer scienceData scienceEngineeringGeographyMedicineWorld Wide WebArchaeologyElectrical engineeringNursing

Abstract

fetched live from OpenAlex

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.

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.

How this classification was reachedexpand

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)0.009
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.515
Threshold uncertainty score0.295

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0090.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
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 head0.351
GPT teacher head0.554
Teacher spread0.202 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2025
Admission routes1
Has abstractyes

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