The relationship between health literacy and patient activation among frequent users of healthcare services: a cross-sectional study
Bibliographic record
Abstract
BACKGROUND: Frequent users of healthcare services are a vulnerable population that deserves attention due to high costs and negative outcomes such as lower quality of life and higher mortality. Healthcare systems should offer interventions tailored to their needs and to their level of health literacy, including strategies to promote activation. The relationship between health literacy and patient activation remains to be explored. The aim of this study was to examine the association between health literacy and patient activation in a population of frequent users of healthcare services with chronic diseases. METHODS: Cross-sectional data were collected (before randomization) through a clinical trial evaluating a case management intervention in primary care. Participants (n = 247) were recruited from the list of frequent users of 4 Family Medicine Groups (FMG) in the Saguenay-Lac-St-Jean region of Québec (Canada). They completed questionnaires by self-report during an encounter with a research assistant: (1) the Newest Vital Sign (NVS) to evaluate health literacy (independent variable); and (2) the Patient Activation Measure-13 (PAM-13) to evaluate patient activation (dependent variable). The relationship between health literacy and activation was examined using biserial correlations. RESULTS: No association was found between health literacy (independent variable) and patient activation (rb = 0.075, ρ = 0.07) for this population of frequent users of healthcare services. CONCLUSIONS: This study suggests that there is no relationship between health literacy and patient activation among frequent users of healthcare services. TRIAL REGISTRATION: NCT01719991 . Registered October 25, 2012.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".