Assessment of the Benefits and Effectiveness of Information Systems for Drug Use as an Effort to Improve Pharmaceutical Services
Bibliographic record
Abstract
Drug Information Service is the duty and responsibility of a pharmacy in providing pharmaceutical services. There is 8 drug information that must be conveyed by pharmacists to patients, but based on reference data that information has not been conveyed completely. Therefore, in this study, a drug use information system was created. The purpose of this study was to assess the benefits and effectiveness of information systems in improving pharmaceutical services. The method of making the system in this study uses the prototype method. System analysis was carried out using a descriptive survey research method. The population used in this study were patients in one of the clinics in Yogyakarta in December 2021. The results of the study on 1250 respondents to assess the benefits of information systems, obtained an average score of 67% on giving information on drug use manually, and an average score of 86% on providing information on drug use along with using information systems. Information on drug use that pharmacists rarely convey in the form of information on how to store, side effects, and drug interactions. Assessment of the effectiveness of the information system shows that respondents feel helped by the existence of an information system with good assessment criteria (4 questions) and sufficient (1 question), while the average score is 88%. This study concludes that providing information on drug use accompanied by using an information system can make it easier for patients to understand the information provided to improve pharmaceutical services.
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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 | 0.021 | 0.070 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| 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".