The Pharmacist Role In Improving Medication Adherence In Dialysis Patients: A Systematic Review
Bibliographic record
Abstract
End-stage renal disease (ESRD) is a worldwide concern in this latest generation as the prevalence had been in an increasing trend. This research aims to evaluate the impact of pharmacist interventions on medication adherence and factors related to medications non-adherence among hemodialysis (HD) patients. A literature search was conducted using search engines PubMed, Cochrane Library and ScienceDirect to identify studies that investigate the pharmacist interventions on medication adherence and factors related to medication non-adherence among HD patients. Based on the study objective a qualitative assessment of the final selected articles was made. The quality of the included studies was assessed using the Newcastle-Ottawa scale. Seven studies met the inclusion criteria. The summed overall quality using the Newcastle-Ottawa scale of all the selected paper was good. The majority of the studies were conducted as prospective study design. More than half of the studies (71%) utilized subjective measure which is self-reporting with a validated questionnaire such as Medication Adherence Report Scale (MARS), Brief Medication Questionnaire (BMQ) and Morisky 8-item Medication Adherence Scale (MMAS-8) to measure participants adherence. The selected papers have shown that poor medication knowledge as the main factor for non-adherence towards medications. The most common reported pharmaceutical care done by the pharmacist to improve medication adherence is by providing medication counselling. All the studies demonstrate the positive impact of pharmacist involvement in HD patients to enhance medication adherence. All identified studies prove that clinical pharmacists could play an important role in educating patients which helps them to have a positive attitude towards medications.
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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 | 0.004 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".