Developing education materials for caregivers of culturally and linguistically diverse patients: Insights from a qualitative analysis of caregivers' needs, access and understanding of information
Bibliographic record
Abstract
OBJECTIVES: To explore the information needs of caregivers of culturally and linguistically diverse (CALD) patients, and how they access and understand health information related to the management of their care person's chronic illness(es). BACKGROUND: Caregivers of CALD patients experience greater unmet needs compared to the general caregiver population. They experience many challenges in identifying resources and accessing formal supports to aid in self-management behaviours. METHODS: Eleven caregivers were recruited from outpatient clinics in Québec, Canada. Consenting caregivers participated in one face-to-face or phone interview. A qualitative descriptive design and inductive content analysis were used to identify themes. RESULTS: Caregivers described a "village" approach to caregiving in which more than one individual was involved in patient care. The specific roles ascribed to caregivers defined their information needs. Caregivers described two categories of information needs: perceived and unperceived. Perceived information needs were explicit, and centred on the medical management of illnesses. Unperceived needs were unrecognized knowledge gaps that emerged during interviews and focused on self-care. CONCLUSION: Although caregivers' perceived needs are often met, their unperceived needs remain unmet. Health-care providers should perform need assessments to identify caregivers' unperceived needs, with the aims of providing culturally competent care and ongoing support.
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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 | 0.014 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".