Patients' experiences of medication management while navigating ongoing care between outpatient services: A qualitative case study of patients on hemodialysis
Bibliographic record
Abstract
Background: Patients on hemodialysis have complex medical diagnoses and medication regimens, requiring access to numerous health services and consultation with various healthcare providers. While interprofessional collaboration can optimize care among hemodialysis patients, these patients commonly experience medication-related problems and frequent hospitalizations resulting from miscommunications and mismanagement of medications. Objectives: This study aims to capture the lived experiences of patients on hemodialysis to reveal their medication management needs as they navigate ongoing care between various outpatient services. Methods: A qualitative methodology was used to explore the perspectives of hemodialysis patients. One-on-one, in-person, semi-structured interviews were conducted at an outpatient hemodialysis clinic located inside an urban teaching hospital. English-speaking adults 18 years and older who have been followed at the clinic for at least three months were selected through random, convenience sampling. Interviews were recorded and transcribed verbatim. Patients were recruited and data were collected iteratively and continued until data saturation was reached. Data was analyzed through the lens of the Picker Principles of Patient Centered Care using a general inductive approach. Results: A total of nine interviews were conducted. Two major themes, medication management and care navigation, were identified. Though patients had a wealth of knowledge about their medications, and they were motivated to self-manage their medications to enhance their well-being, they experienced barriers with medication management. Patients further expressed challenges with navigating care and spoke of the importance of having good rapport with healthcare providers who are attentive to their needs. Conclusions: The results revealed a need for improved support for self-care and interprofessional collaboration to possibly reduce the burden of medications and care fragmentation experienced by patients and improve continuity of care for patients.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".