Digital health literacy: resources and challenges of cancer survivors in Switzerland
Bibliographic record
Abstract
Abstract Background The number of cancer survivors (CS) in Switzerland continues to grow. In parallel, the rapid digital transformation has led to numerous technology-enabled resources to improve the accessibility, scalability, and cost-effectiveness of care. However, CS need digital health literacy (DHL) to be able to find, access and use these resources. We aimed to identify DHL resources and challenges among CS in Switzerland to inform DHL-strengthening interventions. Methods The cross-sectional mixed-methods study was informed by the Optimizing Health Literacy and Access (Ophelia) process. An online survey among CS using the eHLQ covering seven domains was complemented by qualitative interviews to understand CS’ perceived resources and challenges when using and accessing digital health information and services. Quantitative analysis included a cluster analysis to identify subgroups of CS with different DHL profiles. These profiles will be enriched by interview data to develop vignettes of how CS access and use digital health information and services to inform intervention development. Results 131 CS (79% female) completed the survey. In most eHLQ domains, participants show medium to high DHL. The 6-cluster solution describes groups ranging from one characterised by high eHLQ scores and older men without other chronic conditions, working in a health profession, and reporting high self-management (SM) skills, to a group scoring lowest on most dimensions, including mainly older women with low perceived SM skills and greater variability in all other demographic characteristics. Conclusions Our findings indicate that there are groups of CS in Switzerland with a medium to high DHL with a notably good knowledge about health and suggest that gender, experience in healthcare, comorbidities, and SM play key roles in shaping DHL. Using the interview data to build vignettes will help characterize the clusters in more detail.
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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 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".