An Exploratory Study of Person-Centered Care in a Large Urban Hemodialysis Program in Canada Using a Qualitative Case-Study Methodology
Bibliographic record
Abstract
BACKGROUND: Person-centered care (PCC) can benefit patients, clinical staff, and health care organizations, but has not yet been widely adopted into practice. Hemodialysis is a unique care environment in which clinical staff can be involved with patients for protracted periods of time each week and often over a number of years. While kidney care is arguably more holistic than other chronic condition management programs, most patients requiring hemodialysis do not receive care that is optimally person-centered. OBJECTIVE: The purpose of this research was to explore how care is experienced and provided in a large urban hemodialysis program in western Canada in relation to key principles of PCC. In addition, we wanted to understand what factors at an individual, unit, and organizational level facilitate or inhibit PCC in this environment. METHODS: We used a qualitative case-study approach to explore multiple perspectives of care provision using a number of data sources including semi-structured interviews with patients, family members, clinical staff, and administrative staff, as well as observing patterns of clinical practice in local hemodialysis units. FINDINGS: In our study of a single hemodialysis program, we found limited evidence of PCC. Overall, patients reported that their care was good and they had positive relationships with their care team. However, they did not feel involved in decisions regarding their care or consider it to be individualized. In general, providers acknowledged the potential benefits of PCC but were constrained in their practice by a number of factors, including individual perceptions of their role, a prescriptive care environment, and an organizational focus on managing demand. CONCLUSIONS: Evidence of PCC within hemodialysis services was limited, with a number of individual, unit level, and organizational barriers mitigating against its adoption and spread.
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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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.024 | 0.010 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".