Prevalence and predictors of polypharmacy in Jordanian hospitalised patients: A cross‐sectional Study
Bibliographic record
Abstract
OBJECTIVES: This study aimed to evaluate the prevalence and predictors of polypharmacy in hospitalised patients in Jordan to help guide healthcare efforts in decreasing the burden on the healthcare system. METHODS: This cross-sectional study was conducted at the University of Jordan Hospital in Amman, Jordan. During the study period, a convenience sample of patients admitted to the internal medicine and surgical wards were approached to take part in this study. Following patients' recruitments, patients were interviewed and their medical files were reviewed to obtain demographic and clinical information regarding their medical conditions and their regular use of medicines. Then, the prevelence of patients with polypharmacy were identified, and factors predicting polypharmacy among them were determined. RESULTS: Among the 300 participants who agreed to participate in this study, females represented 45.3% of the recruited sample (n = 139), and around 48.0% (n = 144) of the study sample were elderly people (≥65 years old). Most of the recruited patients (n = 248, 82.7%) were found to use polypharmacy (≥ 5 medications). Hypertension was the most frequent medical condition among study participants (n = 240, 80.0%) followed by diabetes (n = 185, 61.7%). Results of logistic regression analysis showed that polypharmacy was only significantly affected by patients' age (OR = 2.149, P-value = .024) and monthly income (OR = 0.336, P-value = .009), while other factors were not associated with polypharmacy. Elderly patients (≥65 years) were found to have polypharmacy more significantly than non-elderly patients. Also, those with lower monthly income (<500 JD) were found to use lower polypharmacy compared with those with higher monthly income (>500 JD). CONCLUSION: The present study showed that polypharmacy is prevalent among patients in Jordan. While polypharmacy was not affected by smoking status, gender, BMI and educational level, it was significantly affected by monthly income and age. Further plans should be put in place to reduce polypharmacy, starting with effective pharmaceutical care services leading to treatment optimisation and ensuring desired treatment outcomes.
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How this classification was reachedexpand
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 | 0.002 | 0.031 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".