Cannabis Consumption and Stigma Labels Among Consumers in a Rural Community in Ebonyi State, Nigeria
Bibliographic record
Abstract
This study examined the influence of labels used to stigmatise cannabis consumers on the control of cannabis consumption in a Community in Ebonyi State, Nigeria. Three research objectives were raised to guide the study. The questionnaire was used to get information on the socio-demographic variables of the respondents. Focus group discussion (FGD) sessions was thereafter conducted in five different places (N = 55, n = 11). Responses from study participants to FGD questions were transcribed verbatim. Three themes emerged in the process. These themes include stigma labels and its deterrent effectiveness on cannabis consumption, stigma labels and differentiation of cannabis consumers from non-consumers and stigma labels and deterrence of public consumption of cannabis. It was found among other things that labels were often used by non-cannabis consumers to stigmatise the cannabis consumers with derogatory name calling as its major preoccupation. This was not an effective tool in deterring cannabis consumers from cannabis use. It was recommended that other researches should focus only on cannabis users to see how they respond to it. It was concluded that counselling should be extended to cannabis users who live in the rural areas of Nigeria.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".