[Drug prescription practices in public medical centres in Gabon].
Bibliographic record
Abstract
CONTEXT OF THE PROBLEM: Inappropriate drug use constitutes a major problem of health programs in developing countries. The objective of this study was to describe and assess the quality of drug prescription practices in public medical centres in Gabon. METHOD: Using the Drug Use Prescribing Indicators developed collaboratively by the World Health Organization (WHO) and the International Network for Rational Use of Drugs (INRUD), we retrospectively evaluated the drugs prescribed during 1,200 randomly selected outpatient consultations that took place in 20 randomly selected public medical centres during the period between July 1, 1997 and June 30, 1998. RESULTS: A total of 3,054 drugs were prescribed during the 1,200 consultations under study, for an average of 2.6 (Standard Deviation: +/- 1.2) drugs per consultation. Of all the drugs prescribed, 13.5% (95% Confidence Interval (CI): 12.3-14.7) were prescribed under their international nonproprietary name (INN), while 57.1% (CI: 55.3-58.9) appeared on the national list of essential drugs. 64.5% (CI: 61.8-67.2) consultations resulted in the prescription of an antibiotic, while 21.8 % (CI: 19.5-24.1) led to the prescription of at least one injection. On average, six key drugs out of eleven were available in the medical centres visited. CONCLUSION: The results of this study suggest that drug prescriptions are far from optimal in the public medical centers in Gabon. Among other things, the prescription of drugs under their INN is very low. These findings highlight the need to develop and implement programs that seek to improve the quality of drug prescriptions in Gabon.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".