Ethnic Differences in Health Literacy, Self-Efficacy, and Self-Management in Patients Treated With Maintenance Hemodialysis
Bibliographic record
Abstract
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.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".