MétaCan
Menu
Back to cohort
Record W2099701408 · doi:10.3390/ijerph120910886

Injuries among Artisanal and Small-Scale Gold Miners in Ghana

2015· article· en· W2099701408 on OpenAlexfundno aff
E Kyeremateng-Amoah, Edith Clarke

Bibliographic record

VenueInternational Journal of Environmental Research and Public Health · 2015
Typearticle
Languageen
FieldHealth Professions
TopicOccupational Health and Safety Research
Canadian institutionsnot available
FundersMcGill UniversityUniversity of Michigan
KeywordsIncidence (geometry)MedicineEnvironmental healthOccupational safety and healthGold miningMedical emergencyInjury preventionScale (ratio)Hospital recordsPoison controlSuicide preventionGeographySurgery

Abstract

fetched live from OpenAlex

Artisanal and small-scale gold miners are confronted with numerous hazards often resulting in varying degrees of injuries and fatalities. In Ghana, like many developing countries, there is paucity of information on the causes and nature of the accidents that result in the injuries. The study was a retrospective, cross sectional type that examined the records of injuries of artisanal and small-scale gold miners presented to the emergency department of a district hospital in the Eastern Region of Ghana from 2006 to 2013. The causes, types, and outcomes of reported injuries were analyzed for 72 cases. Occurrences of mining accidents reported in selected Ghanaian media during the year 2007-2012 were also analyzed to corroborate the causes of the accidents. Fractures and contusions constituted the most frequently occurring injuries, with collapse of the mine pits and falls being the most frequent cause of accidents reported both by the hospital and media records. This study shows that though varied degrees of injuries occur among the miners, the potential for serious injuries is substantial. Measures to reduce the incidence of injuries and fatalities should include education and training on the use of safe working tools and means of creating a safe working environment.

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)0.008
metaresearch head score (Gemma)0.001
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 score0.053
Threshold uncertainty score0.562

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.183
GPT teacher head0.493
Teacher spread0.310 · 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

Citations70
Published2015
Admission routes1
Has abstractyes

Explore more

Same venueInternational Journal of Environmental Research and Public HealthSame topicOccupational Health and Safety ResearchFrench-language works237,207