Chronic Illness Management in Culturally and Linguistically Diverse Patients: Exploring the Needs, Access, and Understanding of Information
Bibliographic record
Abstract
In Canada, people from culturally and linguistically diverse (CALD) backgrounds are at a greater risk of developing a chronic illness, and are more likely to experience adverse health effects and challenges in accessing high-quality care compared with Canadian-born individuals. This, in part, has been attributed to having inadequate access to information and resources needed to manage their illness(es). A qualitative descriptive design and inductive content analysis were used to explore the information needs of 24 CALD patients with chronic illnesses. Participants identified medical, lifestyle, and psychosocial information needs. How much information was needed depended on such antecedents as illness trajectory, severity, and perception. Most information needs remained unmet. A number of communication strategies were identified to bridge language barriers that go beyond translation and are based on effective health education strategies. Findings can help health care professionals better identify CALD patients' information needs and provide strategies that go beyond translation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 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 | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".