A quantitative and qualitative analysis of a medication health literacy workshop for newly and recently arrived refugees
Bibliographic record
Abstract
Abstract Introduction Refugees entering the United States are often unfamiliar with the healthcare system and have different medication beliefs. Since 2016, pharmacy students and faculty have been conducting medication literacy workshops to improve knowledge of medications for newly arriving refugees. Objectives The primary study objective was to measure the newly arriving refugees' medication knowledge after a one‐time educational workshop. Methods This was a retrospective quantitative and qualitative evaluation. Participants engaged in a student‐led 90‐min educational workshop utilizing interpreters, translated materials, and demonstrations. Topics included medical definitions, information on getting sick, medication use, and label reading. A translated, postworkshop evaluation included 22 questions grouped into the following categories: demographics (n = 4), medication use (n = 7), label reading (n = 6), access (n = 3), and cultural beliefs (n = 2). Three optional, free‐response questions regarding overall workshop feedback were included. Quantitative data was analyzed utilizing descriptive statistics. Thematic analysis was used to analyze qualitative data. Two independent coders reviewed each free‐response question and discussed any discrepancies for consensus. The study team developed key themes based on the codes. Results Twenty‐one workshops were conducted with 419 participants from 42 countries. Correct responses were highest for medication beliefs (84%), label reading (78%), access (74%), and medication use (73%). Prescription label reading ability was high (86%), while preventative medicine understanding was lower (34%). Three major learning themes developed, including (1) Cultural differences impact medication habits, (2) Knowing provider roles and how to access different services in healthcare settings was important, and (3) Understanding how to read a label was useful. Researchers found that demonstrations were helpful in participants' learning and that additional education on prevention and specific disease states would be useful. Conclusion Newly and recently arrived refugees were able to correctly identify basic medication health information through a medication literacy workshop. Additional classes exploring other topics, including preventative medicine and medications, should be considered.
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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 | 0.023 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".