Heterogenous subtypes of health literacy among individuals with Metabolic syndrome: a latent class analysis
Bibliographic record
Abstract
Objective To explore the heterogenous subtypes and the associated factors of health literacy among patients with metabolic syndrome.Methods A cross-sectional study was conducted, and 337 patients with metabolic syndrome were recruited from Sir Run Run Shaw Hospital in Zhejiang Province from December 2021 to February 2022. The Social Support Questionnaire, Short version of the Health Literacy Scale European Questionnaire (HLS-EU-Q16), and MacArthur Scale of Subjective Social Status were used for investigation. Latent class analysis (LCA) was performed to explore the heterogenous subtypes of health literacy among Metabolic syndrome patients. Univariate analysis and logistic regression were used to identify the predictors of the latent classes.Results The findings of LCA suggested that three heterogeneous subtypes of health literacy among individuals with metabolic syndrome were identified: high levels of health literacy, moderate levels of health literacy, and low levels of health literacy. The multinomial logistic regression results indicated that compared with low levels of health literacy class, the high levels of health literacy class were predicted by age (OR 0.932, 95%CI[0.900-0.966]), socio-economic status (OR 1.185, 95%CI[1.058–1.328]), and social support (OR 1.065, 95%CI[1.012–1.120]). Compared with low levels of health literacy class, the moderate levels of health literacy class were predicted by age (OR 0.964, 95%CI[0.934–0.995]), socio-economic status (OR 1.118, 95%CI[1.006–1.242]), male (OR 0.229, 95%CI[0.092–0.576]).Conclusion The levels of health literacy among patients with metabolic syndrome can be divided into three heterogenous subtypes. The results can inform policy-makers and care professionals to design targeted interventions for different subgroups among patients with metabolic syndrome who are male, at older age, have less social support, and with disadvantaged socio-economic status to improve health literacy.
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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 | 0.008 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".