Assessment of Community Pharmacists’ Involvement in the Rehabilitation of Drug Abuse Victims in Abuja Municipal Area Council, Abuja, Nigeria
Bibliographic record
Abstract
Drug abuse has now become a major public health problem in Nigeria requiring urgent attention. Although drug abuse cut across all age groups, the youths are however the most affected. This study aimed at assessing Community Pharmacists involvement in the rehabilitation of drug abuse victims. The study was carried out in Abuja Municipal Area Council, questionnaires were administered to Community Pharmacists practicing within the Area Council. A total of 176 Community Pharmacists participated in the study, and slightly above a quarter (27.43%) of them had post-graduate degrees. More than three-quarters (79.5%) of the study participants had received training on drug abuse. A total of 89.2% of the study participants had come across persons suspected to be abusing prescription medicines. Almost all (96.6%) of the study participants indicated that they are willing to advise persons suspected to be abusing drugs on the dangers of drug abuse, and 88.1% of the study participants had spoken to clients concerning abuse of prescription medicines. Also, more than three-quarters (80.1%) of the study participants indicated that pharmacists’ role in the prevention of drug abuse is very important. The study has revealed that Community Pharmacists can play an invaluable role in the rehabilitation of drug abuse victims in 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| 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".