An Investigation Into the Knowledge of South African Pharmacists on the Identification and Management of Drug-Drug Interactions
Bibliographic record
Abstract
BACKGROUND: Detecting and reporting drug-drug interactions (DDIs) is an important role of pharmacists. Standard operating procedures (SOPs), that can be used to manage DDIs is not a requirement at pharmacies in South Africa. SOPs create standardized methods of identifying and reporting DDIs. AIM: The aim of this study was to investigate the knowledge of South African pharmacists on the identification and management of DDIs as well as the availability and use of SOPs in the detection and management of DDIs. METHODS: A quantitative approach was used targeting registered pharmacists from two provinces in South Africa, namely Gauteng and KwaZulu-Natal. 153 responses were received after mailing the questionnaire to 200 pharmacists (76.5% response rate). Data was analysed by using Microsoft Excel® and SPSS® (version 23.0). RESULTS: The majority (93.5%) of respondents were able to correctly define. Forty-four percent of respondents were aware of the existence of SOPs in their respective pharmacies. The majority of the respondents (80.4%) were of the opinion that having SOPs in place for the management of DDIs benefit the identification of these in the pharmacy environment. The findings indicated that availability and access of SOPs are the same across all sectors of pharmacy. CONCLUSION: The results show that the majority of participants have a sound knowledge regarding DDIs as well as the importance of reporting them should such events occur. While most pharmacists were not aware of SOPs in their pharmacies, they regarded this as beneficial.
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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 | 0.002 | 0.013 |
| 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) | 0.004 | 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".