Prevalence and Perception of Pharmacists on Dispensing Prescription Only Medication without Prescription in Makurdi, Benue State, Nigeria
Bibliographic record
Abstract
Background: Currently in Nigeria, prescription only medicines are supposed to be dispensed only with a prescription. The study aimed to reveal the prevalence of prescription only medicine without a prescription and the perception of community pharmacist in Makurdi, Benue State Nigeria towards the phenomenon. Method: The study was carried out between February–April 2019 using simulated patient approach and administration of questionnaire on the pharmacists. Result: Forty pharmacies were surveyed and 100% (29) of the respondents were willing to dispense prescription only medicines without a prescription. Among the respondents, 93% (27) said they do see up to an estimated 30% of POMs without a prescription daily. While 19.2% (5) of the pharmacists do dispense POM without a prescription, 42.3% (11) said they do so depend on the level of education or prior exposure of the client to the medication. Only 38.5% (10) said they will not dispense a POM without a prescription. Most of the pharmacists, 93% (27) agreed that dispensing POM without with a prescription holds potentials for drug abuse, drug misuse, drug dependence with attendant economic consequences. Conclusion: A vast majority of practicing community pharmacists in Nigeria are willing and do dispense prescription-only medications without a valid prescription; for a variety of reasons. This is against existing Nigerian pharmacy laws and the national drug policy.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.003 | 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".