A Causal Model of Health Literacy among Thai Older Adults with Uncontrolled Diabetes
Bibliographic record
Abstract
Uncontrolled diabetes among older adults leads to acute and chronic complications that threaten health and life. Health literacy is crucial to managing health and making successful behavior changes for optimal diabetes outcomes. However, a clear understanding of multiple factors and their mechanisms to influence health literacy is lacking. This descriptive cross-sectional study aimed to test a Model of Health Literacy among Thai Older Adults with Uncontrolled Diabetes and examined the influencing pathways of cognitive function, diabetes knowledge, provider-patient communication, empowerment perception, social support, Internet use, and social engagement regarding health literacy. The sample consisted of 259 older Thai adults with uncontrolled diabetes. Data were collected using a demographic data form, the European Health Literacy Survey Questionnaire, the Diabetes Knowledge Scale, the Diabetes Empowerment Process Scale, the Provider-patient Communication Scale, the Social Support Questionnaire, the Montreal Cognitive Assessment-Basic Test, the Internet Use Questionnaire, and the Being Actively Engaged with Society Subscale of the Active Ageing Scale for Thai People. Data were analyzed using descriptive statistics and structural equation modeling with AMOS. The results showed that the model explained 76% of the quality of life variance. Diabetes knowledge and cognitive function directly affected health literacy. Health literacy was indirectly affected by provider-patient communication and empowerment perception through diabetes knowledge, social engagement through cognitive function, and Internet use through cognitive function and diabetes knowledge. Nurses can develop strategies by integrating Internet use and social engagement in empowerment communication programs to improve diabetes knowledge and cognitive function toward higher health literacy in this population.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.013 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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".