A Medication Safety Model: A Case Study in Thai Hospital
Bibliographic record
Abstract
Reaching zero defects is vital in medication service. Medication error can be reduced if the causes are recognized. The purpose of this study is to search for a conceptual framework of the causes of medication error in Thailand and to examine relationship between these factors and its importance. The study was carried out upon an in-depth case study and survey of hospital personals who were involved in the drug use process. The structured survey was based on Emergency Care Research Institute (ECRI) (2008) questionnaires focusing on the important factors that affect the medication safety. Additional questionnaires included content to the context of Thailand's private hospital, validated by five-hospital qualified experts. By correlation Pearson analysis, the result revealed 14 important factors showing a linear relationship with drug administration error except the medication reconciliation. By independent sample t-test, the administration error in the hospital was significantly related to external impact. The multiple regression analysis of the detail of medication administration also indicated the patient identification before administration of medication, detection of the risk of medication adverse effects and assurance of medication administration at the right time, dosage and route were statistically significant at 0.05 level. The major implication of the study is to propose a medication safety model in a Thai private hospital.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".