MétaCan
Menu
Back to cohort
Record W4285825901 · doi:10.31942/jiffk.v17i01.3504

PERAN KONSELING APOTEKER TERHADAP PENGETAHUAN PENGGUNAAN OBAT DENGAN SEDIAAN KHUSUS DI KETANGGUNGAN – BREBES

2020· article· en· W4285825901 on OpenAlexaff
Muhammad Dwi Suprobo, Nia Fadillah

Bibliographic record

VenueJIFFK Jurnal Ilmu Farmasi dan Farmasi Klinik · 2020
Typearticle
Languageen
FieldMedicine
TopicPublic Health and Nutrition
Canadian institutionsWiLAN (Canada)
Fundersnot available
KeywordsPharmacyTest (biology)PharmacistPharmaceutical careFamily medicineMedicinePsychologyNursing

Abstract

fetched live from OpenAlex

ABSTRACT Counseling is a precious job as known as pharmacists have a provide pharmaceutical services. Counseling given to the publics for drug therapy, treatment, and the drugs with special dosage form is very important to increase publics knowledge. The purpose of this study was to increase the awareness of pharmacists to counseling provide to the public, and increase the public knowledge when using the drugs with special dosage form in Ketanggungan – Brebes regency. Fifty respondents who participated in this study with observasional cohort design. The data collection in pre-test and post-test prospective. Analysis test used is non parametric t test using Wilcoxon alternative test. The last education status of respondents was dominated by Senior High School and the monthly visit to the pharmacy was less than five times as 27 respondents. The results obtained from this study are p = 0.001 and the mean difference is 11.22 with pre-test score mean was 27,54 and post-test was 38,76. There are significant results on the knowledge of the use of drugs with special dosage form after the pharmacist provides counseling. Keywords: pharmacist, counsleing, drugs, pharmaceutical care, knowledge

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.042
Threshold uncertainty score0.140

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0420.005

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.

Opus teacher head0.043
GPT teacher head0.298
Teacher spread0.255 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueJIFFK Jurnal Ilmu Farmasi dan Farmasi KlinikSame topicPublic Health and NutritionFrench-language works237,207