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Record W2129686441

An Evaluation of Prescription Writing and Rational Prescribing in Third-Year Medical Students, Faculty of Medicine, Chulalongkorn University

2000· article· th· W2129686441 on OpenAlexaboutno aff
Danai Wangsaturaka, Viroj Wiwanitkit

Bibliographic record

Venuenot available
Typearticle
Languageth
FieldHealth Professions
TopicHealth Sciences Research and Education
Canadian institutionsnot available
Fundersnot available
KeywordsMedical prescriptionQuarter (Canadian coin)Medical educationFamily medicineDescriptive statisticsMedicineDescriptive researchAlternative medicineStatistical analysisPsychologyNursingSociology
DOInot available

Abstract

fetched live from OpenAlex

115 The main objective of this research is to study prescription writing and rational prescribing by third-year medical students, Faculty of Medicine, Chulalongkorn University. This study was designed as a cross-sectional descriptive study. Five case scenarios were presented to 174 third-year medical students who had to prescribe a rational drug for each patient. Prescription forms were marked, then the knowledge scores were recorded and analyzed using descriptive statistical method. Most subjects' knowledge scores could be classified at the level of 'fair'. Only one quarter of all subjects acquired 'high knowledge ' scores. The issue is to consider how to enhance their competencies in prescription writing and rational prescribing. Further detailed research study is recommended in assessment in the clinical years of students ' competency in prescribing for each group of drugs.

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)0.004
metaresearch head score (Gemma)0.012
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Evaluation · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.996
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.012
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
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 head0.197
GPT teacher head0.507
Teacher spread0.310 · 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.

Study designObservational
DomainEvaluation
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

Citations0
Published2000
Admission routes1
Has abstractyes

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