Enhancing primary care capacity in chronic kidney disease management: a quality improvement educational initiative
Bibliographic record
Abstract
BACKGROUND: Gaps in identification, medical management and appropriate referral for patients with chronic kidney disease (CKD) are evident. OBJECTIVE: We designed and implemented an interactive educational intervention (accredited workshop) to improve primary care providers' awareness of tools to support guideline-concordant CKD management. DESIGN: We used the Kern method to design the educational intervention and targeted the accredited workshops to primary care team members (physicians, nurses and allied health) in Alberta, Canada. We conducted anonymous pre-workshop and post-workshop surveys to identify practice-specific barriers to care, identify potential solutions, and evaluate provider confidence pre-intervention and post-intervention. We used non-parametric statistics to analyse Likert-type survey data and descriptive content analysis to categorise responses to open-ended survey questions. RESULTS: We delivered 12 workshops to 114 providers from September 2017 through March 2019. Significant improvements (p<0.001) in confidence to appropriately identify, manage and refer patients with CKD were observed. Participants identified several patient-level, provider-level, and system-level barriers and potential solutions to care for patients with CKD; the majority of these barriers were addressed in the interactive workshop. CONCLUSIONS: The Kern model was an effective methodology to design and implement an educational intervention to improve providers' confidence in managing patients with CKD in primary care. Future research is needed to determine if these perceived knowledge and confidence improvements affect patient outcomes and whether improvements are sustained long term.
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 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 | 0.014 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".