How Are Albertans “Adjusting to and Coping With” Dialysis? A Cross-Sectional Survey
Bibliographic record
Abstract
Background: Depression and anxiety are commonly reported (40% and 11%-52%) among adults receiving dialysis, compared with ~10% among all Canadians. Mental health in dialysis care is underrecognized and undertreated. Objective: (1) To describe preferences for mental health support reported by Albertans receiving dialysis; (2) to compare depression, anxiety, and quality-of-life (QOL) domains for people who would or would not engage in support for mental health; and (3) to explore sociodemographic, mental health, and QOL domains that explain whether people would or would not engage in support for mental health. Design: A cross-sectional survey. Setting: Alberta, Canada. Patients: Adults receiving all modalities of dialysis (N = 2972). Measurements: An online survey with questions about preferences for mental health support and patient-reported outcome measures (Patient Health Questionnaire–9 [PHQ-9], Generalized Anxiety Disorder–7 [GAD-7], and Kidney Disease QOL Instrument–36 [KDQOL-36]). Methods: To address objectives 1 and 2, we conducted chi-square tests (for discrete variables) and t tests (for continuous variables) to compare the distributions of the above measures for two groups: Albertans receiving dialysis who would engage or would not engage in support for mental health. We subsequently conducted a series of binary logistic regressions guided by the purposeful variable selection approach to identify a subset of the most relevant explanatory variables for determining whether or not people are more likely to engage in support for mental health (objective 3). To further explain differences between the two groups, we analyzed open-text comments following a summative content analysis approach. Results: Among 384 respondents, 72 did not provide a dialysis modality or answer the PHQ-9. The final data set included responses from 312 participants. Of these, 59.6% would consider engaging in support, including discussing medication with a family doctor (72.1%) or nephrologist (62.9%), peer support groups (64.9%), and talk therapy (60%). Phone was slightly favored (73%) over in person at dialysis (67.6%), outpatient (67.2%), or video (59.4%). Moderate to severe depressive symptoms (PHQ-9 score ≥10) was reported by 33.4%, and most respondents (63.9%) reported minimal anxiety symptoms; 36.1% reported mild to severe anxiety symptoms (GAD-7 score ≥5). The mean (SD) PHQ-9 score was 8.9 (6.4) for those who would engage in support, and lower at 5.8 (4.8) for those who would not. The mean (SD) GAD-7 score was 5.2 (5.6) for those who would engage in support and 2.8 (4.1) for those who would not. In the final logistic regression model, people who were unable to work had 2 times the odds of engaging in support than people who are able to work. People were also more likely to engage in support if they had been on dialysis for fewer years and had lower (worse) mental health scores (odds ratios = 1.06 and 1.38, respectively). The final model explained 15.5% (Nagelkerke R 2 ) of the variance and with 66.6% correct classification. We analyzed 146 comments in response to the question, “Is there anything else you like to tell us.” The top 2 categories for both groups were QOL and impact of dialysis environment. The third category differed: those who would engage wrote about support, whereas those who would not engage wrote about “dialysis is the least of my worries.” Limitations: A low response rate of 12.9% limits representativeness; people who chose not to participate may have different experiences of mental health. Conclusions: Incorporating patients’ preferences and willingness to engage in support for mental health will inform future visioning for person-centered mental health care in dialysis.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".