Smoking Prevalence, Willingness to Quit and Factors Influencing Smoking Cessation among University Students in a Western Nigerian State
Bibliographic record
Abstract
Background: In order to increase the proportion of successful attempts to quit smoking, it is important to understand the characteristics of smokers who successfully quit smoking. This study seeks to find out the smoking prevalence, the level of willingness to quit and factors influencing smoking cessation among university students in a western Nigerian state.Methodology: This study was a descriptive cross-sectional study carried out among young adults in tertiary institutions. A sample size of 300 was determined using Fishers formula while multi stage sampling technique was used to select respondents. The questionnaire was semi-structured, pretested and self administered. Analysis was done using Epi-Info version 3.4.1. Frequency tables and cross-tabulations were generated with a statistical significance p-value pre-determined at less than 0.05. Results: The number of respondents that ever smoked was 66 (22% of the total number of respondents) out of which 25 (38%) have ceased smoking while 41 (62%) currently smoke. Those willing to quit out of the 41 that currently smoke are 16 (39%) while 25 (61.0%) were not willing to quit. Of the respondents that ever smoked, the main location of smoking was parties/clubs (50%), while friends (53%) were found to be the main influence to smoke. Willingness to quit smoking was expressed by 16 (39.0%) of current smokers. Among respondents that ever smoked, 55(83.3%) attempted to quit out of which 41(74.5%) did as a result of health problems. Factors that positively affected smoking cessation were older age group of 26-30 (100%), belief that smoking can lead to premature death (47.1%) and never being asked to quit smoking (68.4%) with statistically significant p values. Conclusion: Influence of friends and going to parties/clubs are major factors contributing to smoking habit. Diagnosis of health problems play a major role in attempts to quit smoking while belief that smoking can lead to premature death is a major factor influencing smoking cessation. Being asked to quit smoking without a good understanding of the attendant health hazards does not contribute positively to successful smoking cessation. Peer education in schools emphasizing knowledge of the health implications of smoking as well as early diagnosis of smoking related health problems will go a long way in encouraging smoking cessation.
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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 | 0.001 | 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".