MétaCan
Menu
← Back to cohort
Record W3037966087 · doi:10.5539/gjhs.v12n9p24

Occupational Health and Safety of Commercial Motorcyclists in Obollo-Afor: An Adult Education Approach

2020· article· en· W3037966087 on OpenAlexvenueno aff
Matthias U. Agboeze, Ruphina U. Nwachukwu, Michael O. Ugwueze, Maryrose N. Agboeze

Bibliographic record

VenueGlobal Journal of Health Science · 2020
Typearticle
Languageen
FieldHealth Professions
TopicOccupational Health and Safety Research
Canadian institutionsnot available
Fundersnot available
KeywordsOccupational safety and healthCommissionEnvironmental healthNonprobability samplingPoison controlInjury preventionSuicide preventionPublic healthHuman factors and ergonomicsPopulationMedicineBusinessNursing

Abstract

fetched live from OpenAlex

This study investigated Federal Road Safety Commission (FRSC) public education programme as an adult education approach for improving the health and safety conditions of commercial motorcyclists in Obollo-afor, Nigeria. A descriptive survey research design was used for the study. The population of the study comprised of the four hundred and sixty four (464) commercial motorcyclists and FRSC staff, out of which 108 commercial motorcyclists and the 10 Federal Road Safety Corps staff were sampled using purposive sampling technique. The findings of the study include that FRSC public education programme to a high extent can help in the reduction of accidents and injuries involving commercial motorcyclists. The study recommended that FRSC officials should organize regular road safety awareness campaign on the streets, schools, churches and market square.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.026
Threshold uncertainty score0.052

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.111
GPT teacher head0.511
Teacher spread0.400 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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

Citations1
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueGlobal Journal of Health Science→Same topicOccupational Health and Safety Research→French-language works237,207→