Predictors Of Telehealth Use Among Cancer Survivors: Retrospective Study (Preprint)
Bibliographic record
Abstract
<sec> <title>BACKGROUND</title> 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 healthcare for survivors; however, studies evaluating telehealth in this population remain limited. Additionally, concerns persist regarding equity in technology access and digital literacy. </sec> <sec> <title>OBJECTIVE</title> This study aimed to examine demographic factors and patient attitudes influencing telehealth use among cancer survivors compared to the general population. </sec> <sec> <title>METHODS</title> Adult participants were identified from the nationally representative database Health Information National Trends Survey 6 (HINTS 6). Multivariate logistic regression was used to calculate the predictors of telehealth use among cancer survivors. Chi-square tests compared the prevalence of reported reasons of not using telehealth in the last 12 months between cancer survivors and the general population. </sec> <sec> <title>RESULTS</title> A total of 239,557,883 individuals were included in this study, 7.7% of whom are cancer survivors. 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 years 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 non-users, 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. </sec> <sec> <title>CONCLUSIONS</title> 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’ healthcare experiences. </sec>
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".