Patient-related barriers to some virtual healthcare services among cancer patients in the USA: a population-based study
Bibliographic record
Abstract
Objective: To assess the patient-related barriers to access of some virtual healthcare tools among cancer patients in the USA in a population-based cohort. Materials & methods: National Health Interview Survey datasets (2011–2018) were reviewed and adult participants (≥18 years old) with a history of cancer diagnosis and complete information about virtual healthcare utilization (defined by [a] filling a prescription on the internet in the past 12 months and/or [b] communicating with a healthcare provider through email in the past 12 months) were included. Information about video-conferenced phone calls and telephone calls are not available in the National Health Interview Survey datasets; and thus, they were not examined in this study. Multivariable logistic regression analysis was used to evaluate factors associated with the utilization of virtual care tools. Results: A total of 25,121 participants were included in the current analysis; including 4499 participants (17.9%) who utilized virtual care in the past 12 months and 20,622 participants (82.1%) who did not utilize virtual care in the past 12 months. The following factors were associated with less utilization of virtual healthcare tools in multivariable logistic regression: older age (continuous odds ratio [OR] with increasing age: 0.987; 95% CI: 0.984–0.990), African-American race (OR for African American vs white race: 0.608; 95% CI: 0.517–0.715), unmarried status (OR for unmarried compared with married status: 0.689; 95% CI: 0.642–0.739), lower level of education (OR for education ≤high school vs >high school: 0.284; 95% CI: 0.259–0.311), weaker English proficiency (OR for no proficiency vs very good proficiency: 0.224; 95% CI: 0.091–0.552) and lower yearly earnings (OR for earnings <$45,000 vs earnings >$45,000: 0.582; 95% CI: 0.523–0.647). Conclusion: Older patients, those with African-American race, lower education, lower earnings and weak English proficiency are less likely to access the above studied virtual healthcare tools. Further efforts are needed to tackle disparities in telemedicine access.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".