ABSTRACTS of the virtual 2nd Annual African Regional Interest Group Meeting (AfRIG), 11–13 July, 2022
Bibliographic record
Abstract
Background: Pharmacists' provision of tailored educational counselling on medications may improve patients' adherence and level of confidence, self-efficacy, and understanding of how to take their medications.Objectives: To measure and compare patients' level of medication knowledge and adherence before and after pilot implementation of the service.Methods: A 7-months prospective quasi-experimental study with control and intervention groups, was undertaken among adult patients with cardiovascular diseases admitted to WCH-4 west, on at least one chronic medicine prior to admission and at discharge.Patients in the intervention group received interventional discharge counselling by trained pharmacists trained while patients in the control group received usual care.Patients' level of medication adherence and medication knowledge were assessed through interviews within a day of admission and on days 7 and 14 post-discharge.Repeated measures analysis of covariance was used for comparison between the study groups.A p < 0.05 was considered statistically significant.Results: A total of 84 patients comprising 42 patients in each group were evaluated.Female patients accounted for 59.5% (25/42) and 69.0%(29/42) in the control and intervention group, respectively.After controlling for both the level of adherence and medication knowledge at admission, there was a statistically significant difference on days 7 and 14 post discharge, (F [1, 81] = 110.626,p < 0.001, ηp2 = 0.577) and (F [1, 81] = 41.49,p < 0.001, ηp2 = 0.339) respectively.Conclusion: Implementation of a pharmacist-provided discharge counselling service resulted in improvements in the level of medication knowledge and adherence among patients.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.466 | 0.222 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".