Co-Designing a Patient-Facing Version of a Mental Healthcare Pathway for People Receiving Dialysis
Bibliographic record
Abstract
A Community Advisory Committee comprised of nine members with lived experience of kidney failure, identified the need for a patient-facing version of a mental healthcare pathway for people receiving dialysis in Alberta, Canada. Recognizing that healthcare tools to support person-centred care practices should be available in lay language, our team (comprised of Community Advisors and healthcare clinicians and researchers) co-designed a patient-facing pathway: “Your Journey: Coping with and Adjusting to Dialysis.” The Provincial Pathways Unit provided a template for the pathway, and the content was adapted through multiple online and in-person meetings with the Community Advisors. Adaptations were informed by Community Advisors’ insights, and guided by principles of health literacy and centredness. We wrote this paper together to showcase how we worked in partnership, collaborating as a team to co-design a patient-facing version of a mental healthcare pathway for people receiving dialysis. We highlight our co-design, the processes we followed, reflections from our team members, and lessons learned regarding the language used in patient-facing tools, the value of in-person versus online meetings, and the potential barriers to co-design. Our innovative collaboration provided a solution to create a pathway as a comprehensible and accessible tool to those most impacted: people receiving life-saving dialysis who are also experiencing mental health concerns.
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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 | 0.020 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.004 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".