An Effective Training Program for Registered Sales Clerks Who Sell Over-the-Counter Drugs in Japan: A Preliminary Study
Bibliographic record
Abstract
Purpose: The Japanese healthcare system has designated registered sales clerks to sell over-the-counter (OTC) drugs.Because of this, the AEON HAPYCOM Comprehensive Training Organization implemented an education programto train registered clerks in 2014. The program is unique; it consists of both lectures and hands-on workshopcomponents. We conducted this study as part of a self-evaluation designed to improve the program.Methods: Program participants were asked to respond to an evaluation form upon completion. The form wasdesigned to ascertain student perceptions of the program’s components (e.g., themes, lecturers, materials, and thehands-on workshop) as well as its applicability to their practice of selling OTC drugs.Results: We obtained a total of 6,776 responses from 3,388 participants. On average, each of the program’scomponents were rated highly, with the hands-on workshop being rated the highest. There was a weak relationshipbetween the scores for program preparation and its applicability to OTC sales practices.Conclusion: The program (especially the hands-on workshop component) was highly evaluated by participants.However, we determined that enhancements could be made to the hands-on workshop and other mechanisms toencourage participants to prepare before attending the program.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".