Health Literacy Mediates the Relationships of Cognitive and Physical Functions with Health-related Quality of Life in Older Adults: Cross-sectional Observational Study (Preprint)
Bibliographic record
Abstract
BACKGROUND Declines of cognitive function (CF) and physical function (PF) are two irreversible health conditions linked closely to the elderly’s health-related quality of life (HRQoL). Health literacy (HL) represents the abilities for individuals to cope with health issues, and shows a positive effect on HRQoL. This research hypothesized that HL may act as a partial mediator between CF together with PF and HRQoL. OBJECTIVE This research aimed to determine the model of relationships of CF and PF with HL that contributes to the HRQoL of older adults in Hong Kong METHODS Older adults aged 50-80 years old were recruited through social media and community centers from March to July 2021. HL was assessed using the 12-item Short-Form Health Literacy Survey Questionnaire (HLS-SF12). CF was assessed using Montreal Cognitive Assessment (MoCA). PF was assessed using the Senior Fitness Test (SFT). HRQoL was assessed using the 12-item Short Form Health Survey version 2 (SF-12v2). The mediating effects of HL was tested with path analysis based on the proposed theoretical model. RESULTS Totally, 490 older adults completed the survey. Results for direct effects indicated that CF significantly predicted PF (β=.115, SE=.012, p <.001), PF significantly predicted HL (β=.101, SE=.022, p<.001), and HL significantly predicted HRQoL (β=.457, SE=.049, p<.001). Meanwhile, PF significantly predicted HRQoL directly (β=.150, SE=.025, p<.001) as well as indirectly (β=.046, 95% CI [.028, .067]). CONCLUSIONS A mediational model was hypothesized and tested in the current study to explore the relationships of CF and PF with HL that contributes to the HRQoL. These findings partially supported the hypotheses, highlighting the effect of health literacy on improving older adults’ HRQoL under the influences of both CF and PF together. This study may contribute to further empirical research and health promotion intervention programs that aim to improve HL that leads to better quality of life for the elderly population.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".