Identification of core components and implementation strategies for a Conservative Kidney Management Pathway across a complex, multisector healthcare system in Canada using World Cafés and the Theoretical Domains Framework
Bibliographic record
Abstract
OBJECTIVE: Develop a Conservative Kidney Management (CKM) Pathway for patients unlikely to benefit from dialysis. We sought to determine (1) core components of care and (2) implementation strategies across a multisector healthcare system. DESIGN: We used the Knowledge to Action Cycle and the Theoretical Domains Framework to identify barriers and facilitators to CKM. Activities included a current state assessment, World Cafés, interviews, focus groups and readiness for change assessments. SETTING: A provincial initiative in Alberta, Canada. PARTICIPANTS: 282 participants were purposively selected to reflect those involved in the care of patients receiving CKM. This included policy-makers, multidisciplinary healthcare professionals, patients and their family. MAIN OUTCOME MEASURES: Theoretical domains linked to pathway content and implementation strategies. RESULTS: Environmental context and resources, social/professional role and identity, knowledge and social influences were the most influential behaviour change domains identified. The most effective strategies for facilitating behaviour change were identified to be education, training, environmental restructuring and modelling. Core components of care were determined to be guidelines for treating symptoms and disease complications consistent with the philosophy of CKM, timely communication of the choice for CKM, coordination with community services, crisis planning, advance care planning and tools to enhance patients' capacity for self-management and shared decision-making. This resulted in development of Alberta's CKM Pathway, an interactive, digital, decision-support tool consisting of: (1) a patient decision aid; (2) a patient/family portal; and (3) a healthcare professional portal, where all resources can be freely accessed. CONCLUSIONS: The pathway was codesigned by patients and healthcare professionals and involves tailor-made combinations of tools to address unique patient needs and system-community circumstances. Most of the strategies are adaptable to local context and are likely translatable to the implementation of sustainable CKM in other national and international jurisdictions.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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".