Information on the Prevalence and Extent of Alcohol Abuse Among Commercial Tricycle Operators in Calabar
Bibliographic record
Abstract
This study examines the prevalence and extent of Alcohol abuse among commercial tricycle operators in Calabar, Cross River State, Nigeria. Adopting a descriptive quantitative research method, data was collected from 385 participants from 6424 registered tricycle operators in Calabar using a questionnaire. The participants were selected using the convenience and random sampling technique. Data collected from the field were subjected to descriptive statistics. Out of the 385 distributed instruments, 383 were returned and used for data analysis. From the analyzed data, the result revealed that all the participants, 100.00 per cent, have taken alcohol before. 81.46 per cent taken alcohol before while working, Majority of the tricycle drivers, 38.90 per cent believes that alcohol makes them have clearer vision when driving, 31.59 per cent argued that it makes them work longer, 10.97 per cent maintain that it is because they are used to alcohol. 12.27 per cent of the participants take alcohol at every opportunity they get, 18.02 per cent reported drinking every day. Result also revealed that all the tricycle riders have had accidents before, 77.81 per cent were under the influence of Alcohol when the accidents happened. Based on this result, the study concludes that there was a high prevalence of alcohol abuse among tricycle riders in Calabar. Hence there is a need for the enactment of proper laws that determines the legal limit of alcohol among drivers to checkmate the issue of driving under the influence and its attendant consequences.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".