Predictors Of Telehealth Use Among Cancer Survivors: Retrospective Study (Preprint)
Bibliographic record
Abstract
Abstract Background As the number of cancer survivors continues to grow, optimizing long-term survivorship care models has become increasingly important. Telehealth has the potential to improve access to health care for survivors; however, studies evaluating telehealth in this population remain limited. Additionally, concerns persist regarding equity in technology access and digital literacy. Objective This study aimed to examine demographic factors and patient attitudes influencing telehealth use among cancer survivors compared to the general population. Methods Adult participants were identified from the nationally representative database Health Information National Trends Survey 6 (HINTS 6). Multivariable logistic regression was used to calculate the predictors of telehealth use among cancer survivors. χ 2 tests compared the prevalence of reported reasons of not using telehealth in the last 12 months between cancer survivors and the general population. Results A total of 5793 (weighted n=239,557,883) individuals were included in this study, 7.7% (weighted n=18,545,434) who are cancer survivors. 5092 individuals from the general population and 701 cancer survivors were included. Older age was associated with lower telehealth use (adjusted odds ratio [aOR] 0.11; 95% CI 0.02‐0.59 for patients aged ≥65, compared to those under 40 y old). Higher education (aOR 2.55; 95% CI 1.24‐5.27) and heart disease history (aOR 2.52; 95% CI 1.20‐5.28) were associated with increased telehealth use. Employed (aOR 0.46; 95% CI 0.22‐0.97) and retired (aOR=0.37; 95% CI: 0.18‐0.77) cancer survivors were less likely to use telehealth than unemployed individuals. Of the nonusers, over 60% reported that telehealth options were not offered, and 80% preferred in-person visits. Technical issues and privacy concerns were not major factors in utilizing telehealth. Conclusions Despite greater telehealth use among cancer survivors, a negative association between older age and telemedicine utilization persists. Efforts should focus on improving access for older cancer survivors and addressing employment-related factors, patient attitudes, and telehealth availability. Future studies should explore personalized approaches to enhance cancer survivors’ health care experiences. Our findings emphasize the need to address specific factors including age and employment related disparities, patient preferences and telehealth availability to optimize equitable access to telehealth and enhance the delivery of cancer survivorship care.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".