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Record W4404764722 · doi:10.1080/08865655.2024.2428637

Children’s Exposure Factors and Risk Perception of Sanitation Challenges Along the U.S.–Mexico Border

2024· article· en· W4404764722 on OpenAlexvenueno aff
Alma Anides Morales, Diego Huerta, Mónica D. Ramírez‐Andreotta

Bibliographic record

VenueJournal of Borderlands Studies · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicEnvironmental Justice and Health Disparities
Canadian institutionsnot available
FundersNational Institute of Environmental Health Sciences
KeywordsSanitationRisk perceptionGeneral partnershipEnvironmental healthIntervention (counseling)PerceptionGovernment (linguistics)SituatedGeographyPsychologySocioeconomicsEnvironmental planningBusinessMedicineNursingSociology

Abstract

fetched live from OpenAlex

In the rural U.S. - Mexico border towns, transboundary sanitary sewage overflows (SSOs) are of concern. The high concentrations of pathogens present in SSOs poses a threat to the shared ecosystem and communities' health and well-being. Concerns related to an SSO effluent situated adjacent to a school in Naco, Arizona led to a academic-government-school partnership to assess children's exposure factors, environment and health related risk perceptions, and risk communication preferences. A survey administered to school staff (n=9 and parents (n=31) observed a lower hand/object-to-mouth behavior for children ages 4-6 compared to values in the literature, and the need to further assess exposure factors for children over six. While there was a general negative risk perception to SSOs, approximately half of respondents did not have/were not sure of any SSO related events. Using Bioregion/One Health and cross-border governance frameworks, this study highlights the governing barriers that exist during SSO events and underscores the need for community participation, effective intervention, and risk communication strategies.

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)0.001
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.191
Threshold uncertainty score0.334

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.033
GPT teacher head0.344
Teacher spread0.311 · 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

Citations0
Published2024
Admission routes1
Has abstractyes

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