Pharmacist accessibility for non-native Japanese speakers: A cross-sectional study in Japan
Bibliographic record
Abstract
Background: Pharmacists, being one of the more readily accessible primary health care professionals, must ensure accessibility. With growing internationalization, those in Japanese community pharmacies increasingly interact with non-Japanese speakers. This study aimed to understand how Japanese pharmacies can fulfill accessibility needs by accounting for patients' native language and culture. Methods: A cross-sectional survey of community pharmacies in 2 Japanese municipalities (Hirakata and Suma) was conducted between September and October, 2022. The survey questionnaire included questions on "interaction with non-native Japanese speakers", "impressions during dialogue", and "characteristics and preparedness of pharmacies". The responses were analyzed using descriptive statistics. Results: The response rate was 30.9% (46/149) in Hirakata and 50.0% (29/58) in Suma. The frequency of contact with non-native Japanese speakers at least once a month was 47.8% (22/46) in Hirakata and 55.2% (16/29) in Suma. The main purpose of the visits was to obtain prescription medicines. English was the most commonly used language, as seen on the website, patient forms, medicine bags, informational materials, and staff language skills. However, fewer than half of the stores offered this service. Chinese, Korean, and Vietnamese were even less common. Discussion: Pharmacists, often recognized for their accessibility among primary health care providers, demonstrated limited readiness to serve non-native Japanese speakers in the surveyed regions. To enhance service to diverse pharmacy visitors, improved preparedness for linguistic and cultural differences and cultural competence education are recommended. Conclusion: Japanese pharmacies face barriers in providing language-aligned care to service visitors with diverse linguistic and cultural backgrounds. By preparing for linguistic differences and providing cultural competency education, pharmacies can improve accessibility to effectively serve diverse populations.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".