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Record W4415459039 · doi:10.3389/phrs.2025.1608490

Exploring Associations Between WaSH-Related Health Outcomes and Terrorist Activities in the Sahel: A Scoping Review

2025· review· en· W4415459039 on OpenAlexaff
L. Beck, Branwen Nia Owen, Emma Scott, Mirko S. Winkler, Anaïs Galli

Bibliographic record

VenuePublic health reviews · 2025
Typereview
Languageen
FieldSocial Sciences
TopicTerrorism, Counterterrorism, and Political Violence
Canadian institutionsMcGill University
FundersDirektion für Entwicklung und Zusammenarbeit
KeywordsTerrorismScarcityPublic healthOccupational safety and healthPoison controlPoliticsSuicide preventionHuman factors and ergonomics

Abstract

fetched live from OpenAlex

Objectives: The G5 Sahel countries have faced political instability and terrorist activities for over a decade. With the regional lack of water, sanitation and hygiene (WaSH), there is an increased risk of adverse health outcomes. This scoping review aims to document WaSH-related health outcomes associated with terrorist activities, identify gaps in the humanitarian and political response and propose actionable recommendations to address them. Methods: We followed the PRISMA standards, including literature from PubMed and Web of Science. Country-specific timeframes for terrorist activities were used. Results: Data was extracted from 54 out of 2,320 publications on 22 December 2023. While malnutrition and diarrheal diseases were frequently reported as health outcomes - consistent with inadequate WaSH services - the lack of studies directly linking these outcomes to terrorist activities is notable. Only one article explicitly established a direct link between health outcomes and terrorist activities. Conclusion: The scarcity of studies directly linking terrorist activities to health outcomes reveals a significant research gap and highlight the need for more focused investigations into the health impacts of political violence in the Sahel region.

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)0.025
metaresearch head score (Gemma)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.850
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0250.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0060.001
Bibliometrics0.0000.002
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
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 head0.526
GPT teacher head0.526
Teacher spread0.001 · 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.

Study designOther design
Domainnot available
GenreReview

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

Citations0
Published2025
Admission routes1
Has abstractyes

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