Provider Practice Evaluation Survey: Assessment of Primary Care Provider Perspectives on Care Delivery for CKD Patients in Alberta
Bibliographic record
Abstract
Background: Chronic kidney disease (CKD) is highly prevalent in the adult population of Canada, with a steady rise in end-stage renal disease. The objective of this study was to assess current modes of practice regarding CKD patients, and assess barrier and facilitators in primary care for managing and referring CKD patients using electronic consultation (eReferral). Methods: The Provider Practice Evaluation Survey was launched for primary care providers [PCPs (family physicians or general practitioners)] licensed to practice in Alberta. Associations between barriers and facilitators to electronic consultations and clinic practice parameters; and associations between screening for CKD in patients and clinic practice parameters were analyzed. Modified Poisson regression with robust error variance was used to estimate the relative risk (RR) and 95% confidence interval. Results: A total of 48 PCPs responded to the survey. Awareness about the availability of the eReferral tool was more likely to be a barrier to use eReferral for PCPs of South Zone as compared to PCPs from Edmonton (RR: 2.00, 95% CI: 1.07-3.74). Compared to PCPs with >5% CKD patients in their clinical practice, PCPs with 16% to 26% CKD patients were more likely to perceive barriers to use eReferral; including the ease of use for the eReferral tool (RR: 1.62, 95% CI: 1.05-2.51), and limited staff and technical support as a barrier for eReferral (RR: 2.00, 95%CI: 1.18-3.40). There was a negative association between PCPs aged between 40 and 60 years and time constraints as a barrier compared with those younger than 40 years (RR: 0.66, 95% CI: 0.46-0.95). Regarding screening tools (criteria) to diagnose CKD, PCPs who had not used the eReferral tools were less likely to use hypertension (RR: 0.86, 95% CI: 0.75-0.98), diabetes (RR: 0.89, 95% CI: 0.79-0.999), and cardiovascular disease (RR: 0.72, 95% CI: 0.59-0.89) as CKD diagnostic tools compared to those using eReferral nephrology advice request tool. Conclusions: The results will help implement innovative steps to rectify barriers to adoption of the eReferral system and standardized CKD diagnostic guidelines to improve patient care in Canada.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".