Medication literacy status of outpatients in ambulatory care settings in Changsha, China
Bibliographic record
Abstract
Objective To assess medication literacy status and to examine risk factors of inadequate medication literacy of outpatients in ambulatory care settings. Methods Study participants were recruited randomly from outpatient departments in four tertiary hospitals (Xiangya Hospital of Central South University, Second Xiangya Hospital of Central South University, Third Xiangya Hospital of Central South University, People's Hospital of Hunan Province) in Changsha, Hunan, China, between October 2014 and January 2015. Medication literacy was assessed using the Medication Literacy Scale, Chinese version. Demographic and clinical data were collected using structured interviews. Multiple logistic regression analysis was used to estimate the independent effects of demographic and clinical factors on medication literacy. Results Of 465 participants, 425 (91.4%) produced valid responses for analysis. The mean medication literacy score was 8.31 (standard deviation = 3.47). Medication literacy was adequate in 131 participants (30.8%), marginally adequate in 248 (58.4%), and inadequate in 46 (10.8%). The risk of inadequate medication literacy was greater for older and unmarried patients but lower for more educated patients. Conclusion Many Chinese outpatients in ambulatory care have inadequate medication literacy. Greater age, low education, and unmarried status are important risk factors of inadequate medication literacy.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".