Exploring Knowledge, Attitudes, and Perceptions of Young Pharmacists on Telepharmacy Implementation – A Mini Review
Bibliographic record
Abstract
Telepharmacy, a critical subset of telemedicine, has gained prominence as a transformative innovation addressing challenges in pharmaceutical care by enabling the remote delivery of services, including medication dispensing, patient counseling, and therapeutic monitoring. This narrative review investigates the state of telepharmacy in Indonesia, with a focus on the knowledge, attitudes, and perceptions of young pharmacists in the Special Region of Yogyakarta. The findings reveal significant knowledge gaps concerning telepharmacy tools and regulatory frameworks, which adversely influence pharmacists’ attitudes and perceptions. Although young pharmacists demonstrate higher technological proficiency and generally positive attitudes toward telepharmacy, hesitancy persists due to insufficient training opportunities and ambiguities in existing regulations. Telepharmacy offers substantial opportunities to bridge geographical barriers and enhance healthcare access, particularly in underserved and rural regions. However, its implementation is hindered by critical barriers, including regulatory gaps, inadequate technical infrastructure, and limited awareness among stakeholders. Drawing on successful telepharmacy models from the United States, Canada, and Australia, this review underscores the need for targeted educational initiatives, robust infrastructural investments, and regulatory reform to support telepharmacy adoption. Addressing these challenges will position telepharmacy as a vital tool in transforming Indonesia’s healthcare system, fostering equitable access, and advancing pharmaceutical care nationwide.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 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".