Bibliographic record
Abstract
ABSTRACTDuring pandemic, telepharmacy are the cornerstone of pharmaceutical care which allow broader access. This literature review focuses on answering questions about the pharmacist role on telepharmacy and its impact in several countries as well as studies on regulations in each country. This literature review was written using a scoping review approach. Sources of data retrieved from articles with inclusion criteria; full text, observational studies, discussing the role of pharmacists and the influence of telepharmacy in several countries, or reviewing the related and scope of telepharmaceuticals. Literature search used Google Scholar and PubMed databases. Literature evaluation was carried out to ensure the suitability of the article with the criteria that had been set following the purpose of this literature review. Based on a review of 10 articles shows that telepharmacy has been conducted in America, Canada, the Emirates, Indonesia, and Spain in for services in community as well as in hospitals. Telepharmacy is carried out for reconciliation, monitoring, and counseling services as well as drug information using whatsApp media or systems and websites. Telepharmaceutical services are proven to have a positive impact in the form of reducing errors, side effects, length of stay, and costs. Regulations issued by organizations or countries supports pharmacists in providing telepharmacy implementation.Keywords : telepharmacy, pharmacist's role, impact of telepharmacy, telepharmacy regulations
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.030 | 0.003 |
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".