Drug use in Ghana: knowledge, perceptions, and attitudes in a small group of elite student sportspersons
Bibliographic record
Abstract
Summary Study aim: This study assessed the level of knowledge, perceptions, and attitudes of tertiary student athletes on doping issues. Emphasis was placed on the knowledge of substances found on the prohibited list of the World Anti- Doping Agency (WADA) as well as the potential effects and side effects of the drugs when consumed. Material and methods: The study was conducted in elite tertiary student sportspersons who had qualified in their various disciplines to represent Ghana at the 2011 World University Games in Shenzhen, China. Results: The study found that these sportspersons had significantly higher levels of knowledge of socially abused drugs that also appeared on the WADA prohibited list, such as cocaine (90.9%, p<0.01), heroine (90.9%, p<0.01), cannabis (87.9%, p<0.01) and steroids (72.7%, p<0.05), as compared to drugs that are not socially abused. There was also a large gap in knowledge among respondents with regard to specific knowledge about the possible effects and side effects of banned substance. Of these sportspersons, 30% had received some form of education on doping agents; however, there was no significant statistical difference in knowledge between respondents that had received some form of education and those that had not. Conclusions: The findings of this study suggest that sporting authorities would have to intensify their efforts to provide tertiary school athletes with credible and up-to-date information on doping issues. These interventions may also need to be extended to the wider sporting populace.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".