Telehealth use among cancer survivors.
Bibliographic record
Abstract
493 Background: Telehealth use has risen steeply since the COVID-19 pandemic, alongside acceptability among patients and providers. Patients with cancer have reported positive experiences with telemedicine, although concerns of inequity in technology access and digital literacy remain. As cancer survivors (CS) are an older population, it is unknown if CS are less likely to utilize telehealth compared to the general population (GP). Methods: Adult participants were extracted from the nationally representative database Health Information National Trends Survey 6 (3/2022-11/2022). Chi-square tests compared the prevalence of telehealth use in the last 12 months, and logistic regression was used to calculate adjusted odds ratio (aOR) and 95% CI comparing CS vs the GP. We further explored reasons for telehealth visits and perceptions related to telehealth. All calculations were weighted using SAS 9.4. Significance level was set at 2-sided p < 0.05. Results: A total of weighted 239,557,883 individuals were extracted, with 7.7% CS. Adjusting for confounders, the use of telehealth was significantly higher in CS than the GP (Table). Among those who used telehealth, CS were more likely to have telehealth recommended/required by healthcare providers (HCP) and less likely to attribute their use of telehealth to infection concerns or privacy than their non-cancer peers. No difference was observed regarding using telehealth for convenience, including friends/families, technical difficulties, or equivalence with in-person visits between CS vs the GP. Conclusions: CS used telehealth in the last year significantly more than the GP even when controlling for age. Telehealth was more likely to be recommended or required by their HCP, which may be a driver behind increased usage in CS. Digital literacy may not be a barrier to telehealth use in CS, as they did not experience more difficulties in their telehealth visits. Over half of CS did not use telehealth, however, and research is needed to characterize predictors of telehealth use in CS.[Table: see text]
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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 | 0.004 | 0.005 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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".