Chronic disease health literacy in First Nations people: A mixed methods study
Bibliographic record
Abstract
AIM: To explore chronic disease education, self-management and health literacy abilities from First Nations Australian adults with chronic disease through the integration of qualitative and quantitative findings. BACKGROUND: Chronic disease management requires good health literacy abilities to manage long-term health needs. First Nations people have a higher burden of chronic disease although little is known regarding chronic disease health literacy of First Nations people. DESIGN: A concurrent embedded mixed methods study reported using the Consolidated Criteria for Reporting Qualitative Research guidelines. METHODS: Data were collected from First Nations people with one or more chronic diseases living in remote Australia between February-November 2017. Quantitative data (n = 200) were collected using the Health Literacy Questionnaire along with demographic and health data. Qualitative data (n = 20) were collected via face-to-face interviews to examine chronic disease education and self-management experiences. Data were analysed separately then integrated to develop meta-inferences. RESULTS: Poor communication from healthcare providers coupled with low health literacy abilities is a major barrier to both active and successful management of chronic disease. Communicating in medical jargon resulted in individuals being placed in a power differential causing lack of trust and relationship breakdowns with healthcare providers affecting active chronic disease self-management. The perception of inevitability and ambivalence towards chronic disease and the notion of futility towards self-management were concurred with the low level of active engagement in health care. CONCLUSIONS: Yarning is an important strategy used by First Nations people for communication. For nurses, understanding and developing skills in yarning will facilitate cultural safety, communication and understanding about chronic disease self-management in contexts where health literacy abilities are challenged. RELEVANCE TO CLINICAL PRACTICE: Using yarning, and plain language visual aids, and teach-back will readdress the power differential experienced by First Nations people and may also improve understanding of chronic disease self-management.
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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 | 0.022 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".