Relationship of the Sense of Coherence and E-health literacy With Health Anxiety in Older Women
Bibliographic record
Abstract
ObjectivesThis study aims to investigate the relationship of the sense of coherence and e-health literacy with health anxiety in older women.Methods & Materials This is a cross-sectional study on 350 older women from Richmond Hill, Ontario, Canada, who were selected using a convenience sampling method.The health anxiety inventory (HAI), the sense of coherence scale (SOCS), and the eHEALS were employed to collect data.Data analysis was done using the Pearson correlation test (to evaluate the relationships between variables) and multiple linear regression (to identify predictors of health anxiety), conducted in SPSS software, version 27. ResultsThe participants' mean age was 71.43±5.62 years.The sense of coherence (r=-0.35,P<0.001) and e-health literacy (r=-0.42,P<0.001) had a significant negative correlation with health anxiety.Regression analysis showed that both sense of coherence and e-health literacy significantly predicted the health anxiety, with e-health literacy having a stronger impact (β=-0.42,t=-7.89,P<0.001) compared to the sense of coherence (β=-0.43,t=-7.00,P<0.001).The model explained 22% of the variance in health anxiety (R²=0.20,F=4.89, P=0.003).Conclusion The sense of coherence and e-health literacy are significantly associated with health anxiety in older women.Enhancing the sense of coherence and e-health literacy may be effective in mitigating the health-related anxiety in older women.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".