Developing and Tailoring a Person-Centred Pathway for Mental Health Care for People Receiving Dialysis
Bibliographic record
Abstract
INTRODUCTION: Mental health symptoms are underdiagnosed and undertreated among people receiving dialysis treatment. Despite a high prevalence of depression (40%) and anxiety (42%) symptoms in this population, international guidance does not exist. To address this gap, a multi-phase project involved collaboration by diverse groups in Alberta, Canada to develop and tailor a pathway that supports person-centred mental health care for Albertans receiving dialysis. METHODS: This mixed methods patient-oriented research was conducted in two phases. Phase 1 included: (a) an online clinician survey (n = 199), (b) 11 focus groups and 2 interviews involving 10 people with lived experience and 44 clinicians and administrators, and (c) a scoping review of evidence-based pharmacological treatment. Descriptive analyses of the survey data and summative content analysis of qualitative data (written survey comments and data from focus groups and interviews) were conducted to understand current processes, health services, and interventions for mental health care in Alberta Kidney Care for people receiving dialysis, and to determine appropriateness and opportunities of existing mental health services and interventions. The results were used to develop preliminary statements to inform development of the pathway. Attributes of centeredness in health care - being unique, being heard, and shared responsibility - guided pathway development. Phase 2 involved building consensus on these statements via two rounds of modified Delphi surveys (n = 59 and 51 for rounds 1 and 2, respectively), followed by a consensus call on a virtual platform for discussion and voting involving 27 participants. Voters rated their agreement for each statement using a 3-point Likert scale. Consensus was defined a priori as ≥80% agreement by two groups of voters: people with lived experience and clinicians/others. RESULTS: Phase 1 results informed the development of 68 statements in round 1 of Delphi voting; 42 were approved. Based on voter comments, 11 new statements were developed and 23 statements were revised. Round 2 of Delphi voting included 34 statements. A call was held with people with lived experience to understand why they voted differently than clinicians/others. We learned that some statement language was too technical, such as "assessment" or "score." We talked through each statement and people with lived experience verbally approved the intention of all statements. Through this dialogue, and round 2 voting, 20 statements were approved. A consensus call was held, concluding with voting on 5 statements previously not approved by both groups; 3 were approved. In total, 66 statements were approved for use in development of a pathway addressing symptoms of depression and anxiety, as well as coping. Approved statements guided depiction of the pathway as an algorithm for initial conversations, assessment, follow-up (including "red-flags" or urgent referrals), and management with non-pharmacological and pharmacological supports. CONCLUSION: Strategies to ensure person-centeredness provided all involved parties with opportunities to engage in meaningful ways in pathway development, a novel approach which may provide transferable lessons for kidney programs across Canada and internationally.
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 | 0.029 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".