How Are Albertans “Adjusting to and Coping With” Dialysis? A Cross-Sectional Survey
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,003 |
| Études des sciences et des technologies | 0,002 | 0,001 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».