Analysis of the Trend in the Consumption of Psychoactive Substances in Senegal from 2018 to 2022
Bibliographic record
Abstract
Purpose: The aim of the study was to investigate the profile of psychoactive substance users in Senegal from 2018 to 2022. Methodology: These were retrospective descriptive studies of patient consultation and hospitalization records in the 17 psychiatric and addictology care centers in Senegal from 2018 to 2022. This was an exhaustive survey. All usable consultation and hospitalization registers were included in the study. Non-usable consultation and hospitalization registers were not included. Data were collected from January 1 to December 31 of each respective year. Data were cleaned using Excel before analysis. R software version 4.3.3 was used for analysis. Results: Between 2018 and 2022, 26,029 patients were registered in mental health facilities in Senegal. The Fann Psychiatry Department (CHNU Fann) admitted 23.2% of patients and the Thiaroye National Psychiatric Hospital registered 17.6% of drug users. For the year 2019, we found 8259 and in 2021, we had 6607 patients. The male gender was more represented with 14,750 patients, or 90.9%. The age group [25 - 34] was the majority (7013 patients or 39.5%). The majority of patients (17,425 patients, or 84.6%) were followed as outpatients. The drugs were mainly inhaled (96.7%). The most consumed substance was cannabis with 8847 patients, or 54.2%. Withdrawal was the main reason for treatment, 11,614 patients, or 85.7%. Conclusion: In the light of the results of our study, we can say that the use of psychoactive substances is a real public health and development problem in Senegal. The peaks in psychoactive substance consumption preceded the peaks in psychiatric care centers, so we need to promote information, education and communication on the harmful effects of psychoactive substance consumption among the population in general and young people in particular, in order to safeguard the country’s development.
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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 | 0.002 | 0.003 |
| 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) | 0.002 | 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".