Vehicular Emissions and Its Implications on the Health of Traders: A Case Study of Traders in La Nkwantanang Municipality in Ghana
Bibliographic record
Abstract
The study was based on the recognition that although the health conditions of the human population is vital to sustainable living and productivity, some studies have found that road traffic emissions continue to give rise to infectious and chronic diseases. As a result, the study aimed at assessing the implications of vehicle emissions on the health of traders in Madina in the La Nkwantanang Municipality of Ghana where road traffic is very congested and traders are directly exposed to vehicle emissions. To achieve this objective, 300 traders, made up of 150 traders within a distance of 50 meters and 150 traders within a distance beyond 50 meters of the main road were purposively selected to participate in the survey. Data obtained was analysed using descriptive and inferential statistics. The findings show that although a significant proportion of the respondents had good knowledge about the health consequences of their exposure to emissions, they were reluctant to relocate due to their inability to afford a different location and scarcity of urban space. More importantly, frequent coughing, nausea, poor visibility and difficulty in breathing were among the major self-reported health outcomes. The study also found statistically significant difference in the distribution of self-reported health outcomes by distance of respondents from source of vehicle emission. Also, years spent in the occupation and average daily work hours per week were among factors that related significantly with reported cases of respiratory diseases by respondents.
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".