Analyzing the geographic influence of financial inclusion on illicit drug use in Nigeria
Bibliographic record
Abstract
• The national prevalence of illicit drug use is 14.4 %. • The Southwest region is the hotspot of illicit drug use in Nigeria. • Financial inclusion, income, unemployment, and the proportion of young population are significant predictors of illicit drug use. • There is a spatially varying relationship between illicit drug use and the predictors. Nigeria grapples with a formidable public health concern, as approximately 14 million individuals partake in illicit drug use (IDU). This predicament significantly impacts psychiatric disorders, suicides, disability, and mortality rates. Despite previous investigations into predictors and remedies, the role of financial inclusion (FI) remains inadequately explored. Leveraging existing literature on FI and population health, this study asserts that bolstering FI could be instrumental in mitigating IDU prevalence in Nigeria. We employ spatial analysis to scrutinize the influence of FI and other social factors on IDU, revealing a 14.4 % national prevalence with spatial variations ranging from 7 % in Jigawa state to 33 % in Lagos state. Significant IDU hotspots were identified in the southwest states, while cold spots were observed in the Federal Capital Territory and Nassarawa. Multivariate spatial analysis indicates that FI, income, unemployment, and the proportion of the young population are pivotal predictors of IDU nationwide, explaining approximately 67 % of the spatial variance. Given these findings, the study advocates heightened levels of FI and underscores the need for intensified government initiatives to prevent and address illicit drug use.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".