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Record W4389145876 · doi:10.18357/ijcyfs143202321635

THE ROLE AND IMPORTANCE OF AVIATION FUEL IN THE HEALTH-SEEKING BEHAVIOR OF CHILD MIGRANTS LIVING ALONG THE UGANDA–KENYA BORDER AT BUSIA

2023· article· en· W4389145876 on OpenAlexvenueno aff
Fred Henry Bateganya, Rita Nakanjako

Bibliographic record

VenueInternational Journal of Child Youth and Family Studies · 2023
Typearticle
Languageen
FieldEnvironmental Science
TopicEnergy and Environment Impacts
Canadian institutionsnot available
Fundersnot available
KeywordsFocus groupPsychological interventionPanacea (medicine)PsychologyMedicineEnvironmental healthPolitical scienceBusinessNursing

Abstract

fetched live from OpenAlex

This study aimed to understand the health-seeking behaviors of the child migrants, commonly known as Chokola, who live along the Uganda–Kenya border at the town of Busia. The study used qualitative data collection methods: in-depth interviews, life-histories, focus group discussions, and key informant interviews. At the border, Chokola are accorded a marginal status and identity, limiting their health rights. Chokola face many health challenges, some of which arise from risky sexual behaviors and practices. Their health problems include gonorrhea, HIV, malaria, and cholera. The Chokola in our study exhibited specific health-seeking behaviors, with sniffing aviation fuel being the most pronounced. Although this practice was intended to alleviate common ailments and discomfort, it was also reported to have side effects ranging from loss of appetite to early death. Sniffing aviation fuel as a health-seeking behavior is a construction of individuals. Chokola constructions of the efficacy of aviation fuel are inculcated during socialization and are supported by a shared belief in the fuel as a panacea. Scientific views regarding the risks of side effects are irrelevant to them. In terms of access to health services, Chokola are vulnerable and require affirmative action and targeted interventions.

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 score0.013
Threshold uncertainty score0.187

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 head0.015
GPT teacher head0.271
Teacher spread0.256 · 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

Citations3
Published2023
Admission routes1
Has abstractyes

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