Subjective Experiences and Meaning Associated with Drug Use and Addiction in Nigeria: A Mixed Method Approach
Bibliographic record
Abstract
PURPOSE: Nigeria is experiencing increased rate of drug use among young people. Studies have shown a very high rate of drug use and addiction among university undergraduates and this study was aimed at examining the experiences and meanings associated with drug abuse and addiction among university students while also identifying the causative factors of the use of psychoactive substances.METHODS: The study which is a mixed method made use of an adapted and validated version of the drug abuse screening test (DAST-10) scale to measure drug use and emotional intelligence questionnaire was used to measure an aspect of psychosocial functioning and interviews were used to explore the subjective experiences of six participants. Both the purposive and snowballing sampling techniques were employed. The quantitative data generated were coded and entered into the statistical package for social sciences and results were presented using descriptive tables.RESULTS: The results showed no significant relationship and a negative correlation between drug abuse and emotional intelligence (r = -0.229, p> 0.05). The qualitative data was transcribed and coded using thematic coding where themes are extracted from each transcript. The most commonly used substances were codeine (85%), alcohol (75%), cannabis (70%), tramadol (65%), rohypnol (65%), and tobacco (50%). Qualitative data shows that the participants exercised some sort of willpower over the use of psychoactive substances and the major reason for use was to seek a new experience.CONCLUSION: This study brought to the fore the evidence that personal meanings and experiences come into play in taking decisions on drinking or substance use and this should be considered when interventions are planned.
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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 | 0.008 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".