Ethnic Differences in Health Literacy, Self-Efficacy, and Self-Management in Patients Treated With Maintenance Hemodialysis
Notice bibliographique
Résumé
Background: There is a gap in research investigating the potential impact of ethnicity on health literacy, self-efficacy, and self-management in patients treated with maintenance hemodialysis (MHD). Objective: To explore (1) the associations between health literacy, self-efficacy, and self-management among outpatients with kidney failure receiving treatment with MHD, and (2) the differences in health literacy and self-efficacy based on characteristics of ethnicity (ie, physical resemblance and proficiency in the language of the host population), known to be associated with health care access and health outcomes. Design: Cross-sectional Setting: Outpatients receiving MHD at 7 adult hemodialysis centers in Calgary, Alberta from September 2014 to December 2014. Patients: Participants were grouped into 2 groups based on a proposed 4-quadrant framework of a multicultural society. Quadrant 1 comprised outpatients with physical resemblance and first language of the host population (ie, white and English as a first language), whereas quadrant 4 participants comprised outpatients with physical resemblance and first language not of the host population (ie, non-white and first language other than English). A total of 78 patients (n Q1 = 44, n Q4 = 34) were included. Measurements: Heath literacy, self-efficacy, and self-management were measured using the Health Literacy Questionnaire (HLQ), Strategies Used by People to Promote Health (SUPPH), and Patient Activation Measure-13 (PAM-13), respectively. Methods: Convenience sampling was used to recruit participants at each of the 7 adult hemodialysis centers. All participants completed a study package, which included a demographic questionnaire, HLQ, SUPPH, and PAM-13. Spearman rho was calculated to identify correlations between patient activation level and HLQ and SUPPH scores. Independent t tests were performed to identify differences in HLQ and SUPPH scores between Q1 and Q4 participants. Stepwise regression was performed in other analyses to identify predictor variables of patient activation level. Results: Statistically significant correlations were identified between patient activation level and the health literacy domains of “ability to actively engage with health care providers” (r HLQ6 = .535, P < .001), “ability to find good health information” (r HLQ8 = .611, P < .001), and “understanding health information well enough to know what to do” (r HLQ9 = .712, P < .001). There was a statistically significant difference between Q1 and Q4 participants in the health literacy domain of “ability to find good health information” ( P = .048). “Understanding health information well enough to know what to do” and “actively managing health” were included in the final stepwise regression model, F(2, 72) = 32.232, P < .001. Limitations: The cross-sectional design limits the generalizability of the results. The small sample size limits the power to identify significant associations and differences. Although English was not the first language of Q4 participants, all were proficient in English, meaning potential differences of a key subgroup of Q4 (ie, those who did not speak any English) were not captured. Conclusion: The HLQ allowed for the creation of a health literacy profile of patients with end-stage kidney disease receiving treatment with MHD. The findings suggest possible associations between specific domains of health literacy and patient activation. Outpatients’ representative of Q4 receiving treatment with MHD appear to struggle more with finding good health information, which may leave them at a disadvantage in the early phases of their self-management efforts. The findings highlight potential opportunities to better tailor patient care to support patients in their self-management, particularly for patients from ethnic minority backgrounds.
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 | 0,001 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 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 ».