Prevalence of Drug Relapse among Clients in Rehabilitation Centres in North Central Nigeria: Implications for School Counsellors
Bibliographic record
Abstract
Despite sufficient research studies in the field of drug abuse, drug relapse remains one of the salient aspects that has received less attention among research experts. This study thus investigated the prevalence of drug relapse among clients in NDLEA (National Drug Law Enforcement Agency) rehabilitation centres in North Central, Nigeria. A descriptive survey design was adopted for this study. Censors sampling method, that is, only the available clients or respondents (during the conduct of the study) at the rehabilitation centres in the North Central region participated in the study. A researcher-designed questionnaire on "Prevalence of Drug Relapse" was used to collect the relevant data. The instrument had a reliability co-efficient of 0.69 using the test re-test method. All hypotheses were tested using t-test and Analysis of Variance (ANOVA) statistics at a 0.05 level of significance. The main findings of the study revealed that drug relapse is moderately prevalent among clients in NDLEA rehabilitation centres in North Central, Nigeria. In view of this, it was recommended that NDLEA and other stakeholders should intensify efforts in identifying more addicted individuals so that they can go through the rehabilitation process and adjust effectively to their environment and avoid returning to drug/substance use after treatment.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".