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Record W1961801767 · doi:10.3390/ijerph121012679

Understanding the Social Context of the ASGM Sector in Ghana: A Qualitative Description of the Demographic, Health, and Nutritional Characteristics of a Small-Scale Gold Mining Community in Ghana

2015· article· en· W1961801767 on OpenAlexaff
Rachel Long, Elisha P. Renne, Niladri Basu

Bibliographic record

VenueInternational Journal of Environmental Research and Public Health · 2015
Typearticle
Languageen
FieldEngineering
TopicMining and Resource Management
Canadian institutionsMcGill University
FundersFogarty International CenterUniversity of GhanaSchool of Public Health, University of MichiganDepartment of Afroamerican and African Studies, University of MichiganUniversity of Michigan
KeywordsScale (ratio)Context (archaeology)Gold miningEnvironmental healthOccupational safety and healthCommunity healthQualitative researchCommunity health workersGeographySocioeconomicsEconomic growthSociologyMedicineHealth careHealth servicesEconomicsSocial sciencePopulationCartography

Abstract

fetched live from OpenAlex

This descriptive paper describes factors related to demographics and health in an artisanal and small-scale gold mining (ASGM) community in Ghana's Upper East Region. Participants (n = 114) were surveyed in 2010 and 2011, adapting questions from the established national Demographic Health Survey (DHS) on factors such as population characteristics, infrastructure, amenities, education, employment, maternal and child health, and diet. In the study community, some indicators of household wealth (e.g., radios, mobile phones, refrigerators) are more common than elsewhere in Ghana, yet basic infrastructure (e.g., cement flooring, sanitation systems) and access to safe water supplies are lacking. Risk factors for poor respiratory health, such as cooking with biomass fuel smoke and smoking tobacco, are common. Certain metrics of maternal and child health are comparable to other areas of Ghana (e.g., frequency of antenatal care), whereas others (e.g., antenatal care from a skilled provider) show deficiencies. Residents surveyed do not appear to lack key micronutrients, but report lower fruit and vegetable consumption than other rural areas. The results enable a better understanding of community demographics, health, and nutrition, and underscore the need for better demographic and health surveillance and data collection across ASGM communities to inform effective policies and programs for improving miner and community health.

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)0.004
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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.037
Threshold uncertainty score0.213

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
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 head0.263
GPT teacher head0.363
Teacher spread0.101 · 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 designQualitative
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

Citations26
Published2015
Admission routes1
Has abstractyes

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