Bibliographic record
Abstract
Background: The Can-SOLVE CKD Triple I Study identified continuity of care; access to a primary care provider (PCP) in the hemodialysis (HD) unit; and access to care for other medical conditions as key challenges to in-centre HD care (www. betterkidneycare.ca). The Hubs of Care project aims to address these challenges by incorporating health care providers (HCPs) from other settings in the HD unit; firstly, we identify current practice, potential interest and need and desire for different HCPs in HD units. Methods: A cross-sectional self-reported survey administered Feb-May 2021 with HD patients and staff at four academic sites across Canada. Eligible participants included adults fluent in English or French who could complete the survey independently. The survey asked which HCPs are currently in HD units, which additional HCPs would be most useful to add and whether patients are in favor of other HCPs visiting them either virtually or in-person. Additional data were solicited by free text. Preliminary analyses using descriptive, median (IQR) and proportion and summative content analysis, are presented. Results: Surveys were completed by 393 individuals (252 HD patients and 141 HCPs). Eighty-three percent of patients and 34% of HCPs were ≥50 years old. Forty-five percent of patients had been on HD >3 years. The majority of patients (81.5%) and HCPs (91%) agreed that having other HCPs in HD units would be beneficial; both prefer the addition of diabetic specialists/endocrinologists, mental health specialists and podiatrist/foot care specialists (Table 1). Patients indicated a need for cardiologists. Patients (85%) would like to see a PCP in the HD unit; of those, 87% prefer in-person and 13% prefer virtual. Qualitative analysis reveals privacy concerns due to the open concept of HD Units; however, the concept of bringing HCPs into the HD unit is regarded as beneficial and time-saving. Table 1: - Preferences for Type of HCP Patients n=155 Healthcare providers n=103 Type of health care provider N % N % Cardiologist 29 18.7 6 5.8 Diabetes Specialist 21 13.5 37 35.9 Mental health specialist 20 12.9 33 32 Foot care specialist 18 11.6 30 29.1 Dermatologist 17 10.9 6 5.8 Primary care practitioner 17 10.9 20 19.4 Rehabilitation Specialist 5 3.2 17 16.5 Conclusions: In this cross-sectional survey both HD patients and staff identified that, despite privacy concerns, bringing HCPs that provide foot, diabetic and mental health care into the HD unit was a priority with potential for benefit. Funding: Government Support - Non-U.S.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".