Implementation and evaluation of a patient-focused eHealth intervention, My Kidneys My Health, in primary care and general nephrology clinics (Preprint)
Bibliographic record
Abstract
BACKGROUND Care for mild to moderate chronic kidney disease (CKD) entails self-management from patients and clinical support from primary care and nephrology. In response to a gap in resources for this population, My Kidneys My Health was co-developed to support self-management of CKD. While this is a patient-facing tool, health care providers play a critical role in the implementation of patient resources OBJECTIVE This study develops and evaluates strategies to implement My Kidneys My Health into routine primary care and general nephrology clinical care. METHODS Health care providers working in Alberta, Canada who support patients with CKD were invited to participate in our multi-step study, guided by the Quality Implementation Framework. Step 1: we followed qualitative descriptive methodology to identify barriers and enablers to implementation using a directed content analysis and a deductive coding approach. Participants were invited to complete semi-structured interviews from October 2021 to May 2022. Step 2: we identified, prioritized, co-developed, and launched implementation strategies based on behaviour change theory. Participants from Step 1 were invited to use the materials during the implementation period (May to October 2022). Utilization was tracked through Google Analytics and document distribution tracking. Step 3: we conducted follow-up interviews with participants (October to December 2022) to evaluate implementation based on the Reach, Effectiveness, Adoption, Implementation, and Maintenance framework, following the same qualitative approach as Step 1. Effectiveness was out of scope of this study. RESULTS 14 health care providers participated in Step 1 qualitative interviews (42.9% from nephrology clinics, 35.7% from primary care). Participants shared an individual-level readiness and interest in sharing My Kidneys My Health with their patients. The key barriers to implementation included awareness, memory, time, motivation, and innovation accessibility. Implementation strategies were co-designed and implemented by Step 1 participants (i.e., educational sessions and materials, reminders, implementation coaching). 9 health care providers participated in Step 3 qualitative interviews. Participants shared their approach to tailoring implementation based on their patients and integrating the resource into their current practices. The resources developed were highly utilized by participants, with positive feedback on their usability. Participants expressed motivation to continue sharing My Kidneys My Health; however, awareness and accessibility require further adaptations can improve sustainability of implementation. Our rigorous approach allowed us to address behaviour change and sustainability of implementation of My Kidneys My Health, as well as identifying appropriate and tailored implementation strategies CONCLUSIONS There is a readiness to implement self-management supports for patients with early-stage CKD. A theory-informed approach and strategic implementation strategies can support sustainability. CLINICALTRIAL n/a INTERNATIONAL REGISTERED REPORT RR2-https://doi.org/10.1007/s43477-022-00038-3
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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 | 0.028 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".