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Record W2790770101 · doi:10.5430/rwe.v9n1p1

A Study of Customers’ Behavior in the Use of Pharmaceutical Services – Drugstores in the South of Vietnam

2018· article· en· W2790770101 on OpenAlexvenueno aff
Chia‐Nan Wang, Dinh-Chien Dang, Nguyễn Văn Thành, Pham Ky Quang

Bibliographic record

VenueResearch in World Economy · 2018
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicOrganizational Management and Leadership
Canadian institutionsnot available
Fundersnot available
KeywordsMarketingProduct (mathematics)BusinessFocus groupQualitative researchAdvertisingSociology

Abstract

fetched live from OpenAlex

The current situation in Vietnam is that patients with their illness can easily go to drugstores to buy medicine by their own prescriptions or drugstore ones. This exists for a long time. This research is not focus on the ways to combat this phenomenon, but we aim to study the factors that are affecting behaviors of patients in choosing drugstores in the South of Vietnam. We employed both quantitative and qualitative methodologies for this research; 400 people responded to survey questions and 10 people involved in interviews. Briefly analysis from quantitative study showed that the affected factors are price and process, which is convenient, and people answered to interview agreed that price and product are the important factors for them to go to drugstores. Hence, the sales and policies makers should pay attention to the needs of customers in doing business of drugstores.

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)0.003
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.034
Threshold uncertainty score0.067

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0020.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.266
GPT teacher head0.384
Teacher spread0.118 · 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
Published2018
Admission routes1
Has abstractyes

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