The severity of Tramadol misuse among youth in urban informal settlements in Ghana: patterns, co-use, and sociodemographic factors
Bibliographic record
Abstract
BACKGROUND: Tramadol misuse poses a significant public health and safety risk globally. However, research on this topic is limited in informal settlements, particularly among youth in sub-Saharan Africa (SSA). We assessed the trends, frequency, and social factors associated with the severity of Tramadol misuse among the youth residing in urban informal settlements in Ghana. METHODS: Cross-sectional data from 200 individuals who use Tramadol aged 15–35 years from selected communities in the Asokore Mampong Municipality were analyzed. The severity of Tramadol misuse was assessed with the item “Have you ever used Tramadol for any reason other than medical? If yes, how often do you take Tramadol?” with a 5-point response scale. Multivariable ordinal logistic regression models evaluated the associations between sociodemographic factors and the severity of Tramadol misuse. RESULTS: Tramadol was used daily (44.5%), multiple times (68.5%), and in combination with other substances, mainly energy drinks (60%) or alcohol (12.5%). Our regression models showed that being younger (aOR = 0.87; 95%CI = 0.42–0.98); male (aOR = 1.87; 95%CI = 1.16–2.29); unemployed individuals (OR = 1.02; 95%CI = 1.50–2.86), and having low income (GH¢300–699[US$19.3–45]: aOR = 2.16; 95%CI = 1.01–4.65); < GH¢300[US$ 19.3]: aOR = 2.59; 95%CI = 1.13–5.95) were associated with higher odds for severity of Tramadol misuse. Having higher education (high school: aOR = 0.64; 95%CI = 0.25–0.96); tertiary (aOR = 0.58; 95%CI = 0.23–0.89), lowers the odds for the severity of Tramadol misuse. CONCLUSION: Challenged social well-being indicators relate to the severity of Tramadol misuse among youth in urban informal settlements in Ghana. Interventions of regular drug surveillance, drug use educational programs, and the creation of gainful employment may help reduce Tramadol and other substance misuse among youth living in informal settlements.
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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 | 0.000 | 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) | 0.001 | 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".