Evaluation of the Factors Associated with Prescribed and Non-PrescribedMedicine: A Population-Based Study
Why this work is in the frame
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Bibliographic record
Abstract
OBJECTIVES: Several factors influence medication patterns. The purpose of this study was to look into the role of social determinants in the use of prescribed and non-prescribed medications in a population-based setting of people over 18 in a southern metropolis of Iran (Shiraz) for 2 years. STUDY DESIGN: Prospective population-based cross-sectional. METHODS: This descriptive and cross-sectional survey was done in 2018-2020. A total of 1016 participants were randomly selected based on their postal codes and recruited to the study. The demographic characteristics (age, sex, and education), social profiles (insurance, supplementary insurance, health status, and daily exercise plan), and outpatient visits (family/general physician or specialist/ subspecialist) were recorded by gathering sheets. Descriptive analyses and multinomial logistic analyses were carried out using SPSS software. RESULTS: The medication use pattern was classified into three categories: non-prescribed type I, non-prescribed type II, and prescribed. The mean age of participants was 45.54 ± 15.82 years. The results indicated that most of them took their medication without a prescription (non-prescribed type II). However, people who had insurance and referred to a family physician commonly used the prescribed medications. This study also found that patients who visited a family doctor or a general practitioner used fewer prescribed drugs than those who visited a specialist. CONCLUSION: This study describes social determinants as additional effective factors in health services that influence the use of prescribed and non-prescribed medications in Shiraz. These evidence- based findings can help policymakers to plan the best programs.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.000 | 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 it