Using a Logic Model to systematically evaluate an initiative to improve patient transition to home dialysis therapies (HDTs)
Bibliographic record
Abstract
BackgroundPatients with chronic kidney disease (CKD) often have complex medication regimens and are at a high-risk of drug therapy problems (DTPs). In 2016, a consensus list of renal quality indicator drug therapy problems (QI-DTPs) was developed to aid renal pharmacists in improving the quality of renal pharmaceutical care. Recent research demonstrated that renal pharmacists felt that knowledge gaps could be potential barriers but believed that implementation of these QI-DTPs could lead to better patient care. This study assessed patient preferences and priorities surrounding the type of medication information they require in order to make decisions about drug therapy and to understand if patientsu2019 priorities align with the list of QI-DTPs and current Canadian renal pharmacy practice.Objectivesu2022tTo determine the type of information renal patients require to make decisions about drug therapy.u2022tTo determine the type of medication-related information renal patients would like to enable them to adhere to their medication regimen. u2022tTo obtain patient input on a previously developed list of renal pharmacist QI-DTPs. u2022tTo help inform the development of an intervention to increase the uptake of renal pharmacist QI-DTPsMethodsThe study design was prospective and qualitative research conducted utilizing semi-structured interviews with 10 patients with CKD. The results were analyzed using coding and thematic analysis.ResultsPatients want to learn about medication side effects and expected benefits. They find medical terminology and increasing volumes of paperwork to be unhelpful. Patients stated that additional information or discussion about benefits would not help optimize medication adherence. The QI-DTPs were of high priority to patients. Patients expect their medications to slow the progression of CKD and improve their health. Conclusions Themes emerged including types of useful, unhelpful and sources of information as well as barriers and enablers to adherence to medication and priorities of QI-DTPs of co-morbid conditions.
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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 | 0.058 | 0.110 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".